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Addressing Climate Change in Transport

Volume 2: Pathway to Resilient Transport

Vietnam Transport Knowledge Series Supported by AUSTRALIA–WORLD BANK GROUP STRATEGIC PARTNERSHIP IN VIETNAM and NDC PARTNERSHIP SUPPORT FACILITY

Addressing Climate Change in Transport Volume 2: Pathway to Resilient Transport

Jung Eun Oh, Xavier Espinet Alegre, Raghav Pant, Elco E. Koks, Tom Russell, Roald Schoenmakers, and Jim W. Hall

FINAL REPORT September 2019

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Cover photo: A motorcycle crosses over a flooded road in rural Vietnam. Hanoi Photography – stock.adobe.com.

Contents

Figures and Tables ...... vii Foreword...... xi Acknowledgments ...... xiii Abbreviations and Acronyms...... xvii Executive Summary ...... 21 Chapter 1: Introduction ...... 29 Background and Objectives ...... 29 Scope of the Study ...... 31 Study Contributions and Limitations ...... 33 Report Structure ...... 36 Chapter 2: Vietnam Profile: Transport Network and Exposure to Natural Hazard...... 39 Transport Networks...... 39 National-scale road infrastructure networks ...... 39 Province-scale roads networks...... 40 Railway infrastructure network...... 41 Inland waterway infrastructure network ...... 41 Maritime infrastructure network ...... 42 Air transport infrastructure network...... 42 Modeling Freight Flow on Transport Networks ...... 42 Results of freight flow mapping at national scale...... 42 Results of province-scale flow mapping ...... 45 Uncertainties in freight flow mapping ...... 48 Natural Hazard Exposure of Transport Networks ...... 50 National-scale road and railways hazard exposures...... 51 Province-scale hazard exposure of roads ...... 57 Chapter 3: Vulnerability, Criticality, and Risk Assessment...... 65 Vulnerability and Criticality Assessment ...... 65 National-Scale Road Network Criticality...... 66 National-Scale Railway Criticality ...... 69 Province-Scale Road Criticality ...... 71 Lao Cai province roads disruptions...... 71 Binh Dinh province roads disruptions...... 72 Thanh Hoa province roads disruptions...... 73 Risk Assessment...... 74 National-scale networks...... 75 Province-scale analysis...... 81 Uncertainties in Criticalities and Risks...... 86 Criticalities and Vulnerabilities of Ports...... 90

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Chapter 4: Adaptation Strategy and Analysis...... 93 Scope and Purpose of Adaptation Analysis...... 93 Identifying Failures by Climate Change Driven Hazard Threats...... 94 Adaptation Options and Costs...... 95 Results of Adaptation Analysis ...... 97 National-scale road network adaptation costs and benefits ...... 98 Province-scale road network adaptation costs and benefits ...... 104 Decision-Making under Uncertainty: Sensitivity of Costs of Adaptation...... 111 Ranges and uncertainties in adaptation estimates...... 112 Chapter 5: Exploring Multimodal Options for National-Scale Transport...... 117 Including Multimodal Links and Costs...... 118 Results of Partial Multimodal Shifts in Road Failure Analysis ...... 119 Rail Failure Analysis Results...... 122 Chapter 6: Conclusions and Policy Recommendations ...... 127 Increasing Vulnerabilities Due to Climate Change ...... 127 Prioritizing Transport Network Resilience for Systemic Economic Benefit ...... 127 Making the Case for Investment in Adaptation to Build Transport Network Resilience...... 128 Strong Case for Improving Multimodal Transport Linkages and Enhancing Efficiency across Modes....129 Improving Existing Data Gaps for Further Studies ...... 129 Appendix A: Methodology for Transport Risk and Adaptation Assessment ...... 131 Methodological Framework and Implementation Structure...... 131 Key definitions...... 132 Estimation of adaptation options...... 135 Uncertainty analysis...... 136 Appendix B: Overview of Datasets...... 141 Natural hazards dataset...... 142 Appendix C: Vulnerability Results for Ports...... 145 Appendix D. Communes and Districts by Exposure of Road Networks to Natural Hazards . 151

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Figures and Tables

FIGURES Figure E.1. Maximum Economic Losses and Benefit-Cost Ratios of National Road Networks Due to Transport Disruptions ...... 24 Figure 1.1. Vietnamese Provinces and Neighboring Countries Considered in the Study...... 32 Figure 2.1. Maximum AADF for the National-Scale Transport Network ...... 45 Figure 2.2. Maximum Crop Flows toward Nearest Commune Centers and Maximum Net Revenues on Lao Cai Provincial Roads...... 46 Figure 2.3. Maximum Crop Flows toward Nearest Commune Centers and Maximum Net Revenues on Binh Dinh Provincial Roads...... 47 Figure 2.4. Maximum Crop Flows toward Nearest Commune Centers and Maximum Net Revenues on Thanh Hoa Provincial Roads...... 48 Figure 2.5. Ranges of AADF Flows by Mode ...... 49 Figure 2.6. Ranges of Net Revenue Flows Assigned to Road Networks in Lao Cai, Binh Dinh, and Thanh Hoa...... 50 Figure 2.7. Hazard Exposure of National-Scale Roads Network in Vietnam...... 54 Figure 2.8. Hazard Exposure of National-Scale Railway Network in Vietnam ...... 55 Figure 2.9. Percentage Change in Exposure of National-Scale Road Network to 1,000-Year River Flooding by 2030...... 56 Figure 2.10. Percentage Change in Exposure of National-Scale Rail Network to 1,000-Year River Flooding by 2030...... 57 Figure 2.11. Hazard Exposure of Province Roads Network in Lao Cai...... 59 Figure 2.12. Percentage Change in Exposure of Lao Cai Road Network Links to Landslides by 2050 ... 60 Figure 2.13. Hazard Exposure of Province Roads Network in Binh Dinh...... 60 Figure 2.14. Percentage Change in Exposure of Binh Dinh Road Network Links to 1,000-year River Flooding by 2030...... 61 Figure 2.15. Hazard Exposure of Province Roads Network in Thanh Hoa...... 62 Figure 2.16. Percentage Change in Exposure of Thanh Hoa Road Network Links to 1,000-Year River Flooding by 2030...... 63 Figure 3.1. National-Scale Roads Criticality Results ...... 68 Figure 3.2. National-Scale Railway Criticality Results...... 70

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Figure 3.3. Maximum Estimated Disruption in Net Revenue and Maximum Economic Loss for Lao Cai Province Road Network ...... 72 Figure 3.4. Maximum Estimated Disruption in Net Revenue and Maximum Economic Loss for the Binh Dinh Province Road Network ...... 73 Figure 3.5. Maximum Estimated Disruption in Net Revenue and Maximum Economic Loss for the Thanh Hoa Province Road Network ...... 74 Figure 3.6. Maximum Estimated Risks for National-Scale Road Network Links ...... 76 Figure 3.7. Percentage Change in Maximum Failure Risks of National-Scale Road Network Links for Future 2030 River Flooding under Climate Scenarios ...... 77 Figure 3.8. Maximum Estimated Risks for National-Scale Railway Network...... 79 Figure 3.9. Percentage Change in Maximum Failure Risks of National-Scale Rail Network for Future 2030 River Flooding under Climate Scenarios...... 80 Figure 3.10. Maximum Risks of Lao Cai Province-Scale Road Network...... 81 Figure 3.11. Percentage Change in Maximum Failure Risks of Lao Cai Province-Scale Road Network Links for Future 2050 Landslide Susceptibility under Climate Scenarios ...... 82 Figure 3.12. Maximum Risks of Binh Dinh Province-Scale Road Network...... 83 Figure 3.13. Percentage Change in Failure Risks of Binh Dinh Province-Scale Road Network for Future 2030 River Flooding under Climate Scenarios...... 84 Figure 3.14. Maximum Estimated Risks for Thanh Hoa Province-Scale Road Network ...... 85 Figure 3.15. Percentage Change in Failure Risks of Thanh Hoa Province-Scale Road Network for Future 2030 River Flooding under Climate Scenarios...... 86 Figure 3.16. Estimated Ranges of Daily Economic Losses for Individual Network Link Failures in Vietnam’s National-Scale Road and Rail Networks...... 87 Figure 3.17. Estimated Ranges of Minimum and Maximum Risks for Individual Link Failures Due to Current and Future River Flooding on National-Scale Networks ...... 88 Figure 3.18. Estimated Ranges of Daily Economic Losses and Risks Due to Current and Future River Flooding of Individual Link Failures on the Lao Cai Road Network ...... 89 Figure 3.19. Estimated Ranges of Daily Economic Losses and Risks Due to Current and Future River Flooding of Individual Link Failures on the Binh Dinh Road Network ...... 89 Figure 3.20. Estimated Ranges of Daily Economic Losses and Risks Due to Current and Future River Flooding of Individual Link Failures on the Thanh Hoa Road Network ...... 90 Figure 4.1. Maximum Total Investment over 35 Years on the Climate Adaptation for Identified Links in the National-Scale Road Network in Vietnam...... 99 Figure 4.2. Estimated Maximum Benefits over 35 Years and Maximum BCRs of Adaptation Options for Identified Links in the National-Scale Road Network in Vietnam ...... 100

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Figure 4.3. Comparisons of BCRs of Adaptation Options for National-Scale Road Network Links in Vietnam...... 102 Figure 4.4. Investments, Benefits, and Adaptation BCR Options for Identified Links in the Lao Cai Road Network ...... 105 Figure 4.5. Investments, Benefits, and BCRs of Adaptation Options for Identified Links in the Binh Dinh Road Network ...... 107 Figure 4.6. Investments, Benefits, and Adaptation BCRs Options for Identified Links in the Thanh Hoa Road Network ...... 109 Figure 4.7. Parameter Sensitivity for National- and Province-Scale Roads ...... 111 Figure 4.8. Adaptation Analysis Results for the National-Scale Road Network in Vietnam ...... 113 Figure 4.9. Ranges of Adaptation BCRs for the National-Scale Road Network in Vietnam, Evaluated for Current and Future Extreme River Flooding Scenarios ...... 113 Figure 4.10. Adaptation BCRs for Province-Scale Road Networks ...... 115 Figure 5.1. Multimodal Links Created in the Network Model ...... 118 Figure 5.2. Total Economic Impacts of National-Scale Road Links Failures When Considering 90 Percent Flow Rerouting on Roads and 10 Percent Rerouting along Other Networks via Multimodal Links ...... 120 Figure 5.3. Redistribution of 10 Percent of Maximum Road Tonnage Flows as Added Tonnages onto Rail and Waterway Networks following Road Network Link Disruptions ...... 122 Figure 5.4. Total Economic Impacts of Rail Link Failures when Considering Rerouting Options along National-Scale Roads, Inland Waterways and Maritime Networks via Multimodal Links ...... 123 Figure 5.5. Redistribution of Maximum Tonnage Flows as Added Tonnages onto National-Scale Roads and Waterway (Inland and Maritime) Networks following Rail Network Link Disruptions ...... 124 Figure A.1. Graphical Representation of Infrastructure System-of-Systems Risk Assessment Framework ...... 132

TABLES Table E.1. Climate Adaptation Analysis for Province-Scale Road Networks...... 25 Table 2.1. Road Categories by Average Daily Vehicle Traffic Counts ...... 39 Table 2.2. Estimated Total Lengths by Road Category in the National-Scale Road Network Model for Vietnam...... 40 Table 2.3. Estimated Lengths by Road Category in the Province-Scale Road Network Model for Vietnam...... 40 Table 2.4. Estimated Total Link Lengths and Percentages of Rail Routes Identified in the National- Scale Rail Network Model for Vietnam ...... 41

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Table 2.5. Daily Traffic Volumes by Commodity and Transport Mode...... 44 Table 2.6. Lengths of National Roads and Railway Networks Exposed to Different Types of Hazards . 52 Table 2.7. Exposure of Province Roads Networks to Hazards ...... 58 Table 3.1. Selected Numbers of Single Link Failure Scenarios for Different Transport Networks...... 66 Table 4.1. Potential Failure Types Due to Increased Hazard Threats Driven by Adverse Climate Change ...... 95 Table 4.2. Cost Summary of Initial Adaptation Investment for Building Prototype Climate Resilient Roads in Vietnam ...... 97 Table 4.3. Numbers of Single-Link Failure and Hazard Exposure/Adaptation Options Scenarios Evaluated for Transport Networks in Vietnam...... 98 Table 4.4. Twenty National-Scale Roads Ranked in Descending Order of Maximum Adaptation BCRs ...... 103 Table 4.5. Top 20 Lao Cai Road Assets Ranked in Descending Order of Maximum Adaptation BCRs. 106 Table 4.6. Top 20 Binh Dinh Road Assets Ranked in Descending Order of Maximum Adaptation BCRs ...... 108 Table 4.7. Top 20 Thanh Hoa Road Assets Ranked in Descending Order of Maximum Adaptation BCRs ...... 110 Table B.1. Raw Datasets Collected from Different Data Sources for the Transport Risks Analysis Modeling in Vietnam...... 141 Table B.2. Natural Hazard Datasets Assembled for the Study Notes ...... 143 Table C.1. Airports with AADF Flows and Hazard Exposure Scenario Results...... 145 Table C.2. Major Maritime Ports with AADF Flows and Hazard Exposure Scenario Results ...... 146 Table C.3. Major Inland Waterway Ports with AADF Flows and Hazard Exposure Scenario Results... 147 Table D.1. Twenty Communes Most Exposed to Flooding in Lao Cai Province...... 151 Table D.2. Twenty Communes Most Exposed to Flooding in Binh Dinh Province...... 152 Table D.3. Twenty Communes Most Exposed to Flooding in Thanh Hoa Province...... 153

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Foreword

Climate change is set to have profound effects on Vietnam’s development. With nearly 60 percent of its land area and 70 percent of population at risk of multiple natural hazards, Vietnam globally is among the most vulnerable countries to both chronic and extreme events. Over the past 25 years, extreme weather events have resulted in 0.4 to 1.7 percent of GDP loss, which climate change is predicted to steeply rise by 2050. At the same time, as Vietnam’s economy grows, the country is becoming a significant emitter of greenhouse gases. While Vietnam’s absolute volume of emissions is still small compared to that of larger and richer countries, emissions are growing rapidly and disproportionate to its economy size. Vietnam is the 13th most carbon intensive economy in the world, measured in terms of emissions per GDP, and 4th among the low- and middle-income countries in East Asia.

The transport sector plays a critical role in these recent trends. A steep rise in income and economic growth has led to rapid motorization: The country of around 96 million people is also home to nearly 40 million vehicles, including 35 million motorbikes. While car ownership is still relatively low in Vietnam, as income rises cars are quickly replacing motorbikes, especially in the largest cities. Public transport modal share remains persistently low, partly due to the low level of network development and partly to the convenience and affordability of two-wheeler-based mobility. Thanks to its economic success and rapid integration with the international trade, cargo transportation in Vietnam has seen remarkable growth in the recent years. Vietnam’s long coastal lines and extensive inland waterway network have been extensively used for the movement of goods; however, their modal share vis-à-vis road transport is declining.

Vietnam’s transport network, which has seen an impressive expansion over the past two decades, is increasingly vulnerable to the intensifying climate hazards. Today, Vietnam’s road network extends to over 400,000 km, much of which was not built to withstand extreme hazard scenarios, which are expected to become more frequent due to climate change. Without efforts to improve the resilience of the built network, Vietnam’s achievements in providing universal access to its rural communities may be undermined. Moreover, resilience of connectivity is critical to the continued success of Vietnam’s economy, which heavily relies on external trade and would increasingly depend on seamless rural-urban linkages.

In this analytical work, Addressing Climate Change in Transport for Vietnam, carried out by the World Bank and several other partners with support from the Ministry of Transport of Vietnam, the study aims to set out a vision and strategy for climate-smart transport, in order to minimize the carbon footprint of the sector while ensuring its resilience against future risks. The analytical findings and recommendations are presented in two volumes of the report: Volume 1—Pathway to Low Carbon Transport and Volume 2—Pathway to Resilient Transport. The first volume provides how Vietnam

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can reduce its carbon emission by employing a mix of diverse policies and investments, under varying levels of ambition and resources. The second volume provides a methodological framework to analyze network criticality and vulnerability, and to prioritize investments to enhance resilience.

These two report volumes have been prepared at a critical time, where the Government of Vietnam is working to update its Nationally Determined Contribution and set out its next medium-term public investment plan for the period of 2021 to 2025. We hope that these findings can provide useful insights and specific recommendations towards these critical documents, contributing to Vietnam’s achievement in developing a low-carbon and resilient transport sector.

Guangzhe Chen Ousmane Dione Global Director Country Director Transport Global Practice Vietnam

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Acknowledgments

This report was prepared by the Transport Global Practice and the East Asia and Pacific Region of the World Bank.

The preparation of the report was led by Dr. Jung Eun Oh (Senior Transport Economist) and Dr. Xavier Espinet Alegre (Transport Specialist) at The World Bank, along with Dr. Raghav Pant at Oxford Infrastructure Analytics (OIA). The team includes Ms. Maria Cordeiro, Dr. Jasper Cook, Mr. Duc Cong Phan, Dr. Nguyen Thanh Long, Mr. Chi Kien Nguyen at the World Bank, and Dr. Elco E. Koks, Mr. Tom Russell, Mr. Roald Schoenmakers and Prof. Jim W. Hall at OIA. All crop production outputs are gratefully acknowledged from Dr. Jawoo Koo, Dr. Zhe Guo, Dr. Ulrike Wood-Sichra, and Dr. Yating Ru at International Food Policy Research Institute.

The team extends its appreciation for the guidance of Guangzhe Chen (Global Director, Transport Practice), Franz R. Drees-Gross, (Director, Transport Practice), Ousmane Dione (Vietnam Country Director), Almud Weitz (Transport Practice Manager, Southeast Asia and the Pacific), Achim Fock (Vietnam Operations Manager), and Madhu Raghunath (Vietnam Infrastructure Program Leader).

The team conducted the study in collaboration with the Government of Vietnam, and appreciates the strong support and advice generously provided by Mr. Le Dinh Tho, Vice Minister of Transport, and Mr. Tran Anh Duong (Director General), Ms. Nguyen Thi Thu Hang (Deputy Director), Mr. Vu Hai Luu (official), Ms. Doan Thi Hong Tham (official) and Mr. Mai Van Hien (official) at the Department of Environment, Ministry of Transport (MoT). Several government entities and organizations provided vital inputs: Directorate for Roads of Vietnam (DRVN); Vietnam Inland Waterways Administration (VIWA); Vietnam Maritime Administration (VINAMARINE); Civil Aviation Authority of Vietnam (CAAV); Vietnam Railway Authority (VRA); and Vietnam Register (VR).

The work benefited from the advice provided by the following peer reviewers: Stephen Ling (Lead Environmental Specialist), Cecilia M. Briceno-Garmendia (Lead Economist), Neha Mukhi (Senior Climate Change Specialist), and Ian Halvdan Ross Hawkesworth (Senior Governance Specialist) at the World Bank, and Robin Bednall (First Secretary) and Duc-Cong Vu (Senior Infrastructure Manager) at the Government of Australia. Kara Watkins (World Bank Communications Consultant) thoroughly copyedited the report for consistency and readability, and Chi Kien Nguyen (Transport Specialist) reviewed the Vietnamese translation for accuracy, for which the team is grateful.

The team also appreciates the excellent production support provided by Nguyen Thanh Hang, Nguyen Mai Trang, Ira Chairani Triasdewi (administration), Dang Thi Quynh Nga (operations), and Nguyen Hong Ngan (communications). The team also appreciates the excellent production support provided by Nguyen Thanh Hang, Nguyen Mai Trang, Ira Chairani Triasdewi (administration), Dang Thi Quynh Nga (operations), and Nguyen Hong Ngan (communications).

The team would like to thank the Government of Australia and NDC Partnership Support Facility for their generous support and funding toward this analytical work and publication.

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About the Authors

Jung Eun “Jen” Oh is a senior transport economist at the World Bank. With a background in transport engineering and economics, Jen has led a number of investment projects and technical assistance for various client countries in Southeast Asia, Central Asia, Sub-Saharan Africa, and Europe, covering a broad range of transport sector issues. Jen served as a transport cluster leader for Vietnam from 2016 to 2019, overseeing and coordinating a large portfolio of World Bank-financed projects in Vietnam’s transport sector, and led several knowledge pieces on climate change in transport, transport connectivity, urban mobility, and infrastructure financing. Jen holds an MSc in economics and a PhD in transportation systems, and has authored several articles, including a chapter in the book, The Urban Transport Crisis in Emerging Economies (2017), published by Springer International.

Xavier Espinet Alegre, a transport specialist for the chief economist for sustainable development at the World Bank, has extended expertise in transport planning and economics, climate change adaptation, disaster risk management, and decision-making science. He has worked for more than five years developing interdisciplinary and cross-sectoral solutions, as the Decision-Making under Deep Uncertainties approach, to challenges posed by climate change to transport planning in Mozambique, Sierra Leone, Albania, Vietnam, Cambodia, and Saint Lucia, among others. Prior to the World Bank, he co-founded Resilient Analytics, serving as a technical lead, and worked as a research associate at the Institute of Climate and Civil Systems. Xavier holds a PhD in civil systems from the University of Colorado at Boulder and a civil engineering degree from the Universitat Politecnica de Catalunya in Spain.

Raghav Pant serves as a senior postdoctoral researcher at the Environmental Change Institute (ECI) at the University of Oxford, and is a founder and director of Oxford Infrastructure Analytics Ltd. Raghav has more than 12 years of academic and professional experience on infrastructure and economic network risks modeling, and leads the resilience analysis research within the UK Infrastructure Transitions Research Consortium (ITRC). He has worked with the World Bank on ongoing projects on transport risk analysis in Tanzania and Argentina. His research paper on vulnerability assessment of Great Britain’s railway infrastructure has been awarded the 2016 Lloyds Science of Risk Prize in Systems Modeling.

Elco E. Koks works as a postdoctoral researcher in global network analysis at ECI and assistant professor at the Institute for Environmental Studies at Vrije Universiteit Amsterdam. Elco has more than seven years of experience working on several topics related to disaster impact modeling, climate change, and water economics in the context of national (Knowledge for Climate) and EU research projects (ENHANCE and TURAS). His key research domain is the modeling of the economy-wide consequences of natural disasters on both a regional and interregional level, with a specific focus on industrial areas and critical infrastructure.

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Tom Russell, a research software engineer at the Environmental Change Institute, University of Oxford, has more than seven years of experience in research software development, particularly in spatial network analysis and data-driven design and interaction. Tom is creating the infrastructure systems modeling integration framework for the Infrastructure Transitions Research Consortium (ITRC) and developing open-source tools for infrastructure risk analysis through the Oxford Infrastructure Analytics–World Bank projects on transport risk analysis in Tanzania, Vietnam and Argentina.

Roald Schoenmakers works as a research software developer, actively collaborating with domain experts to facilitate in their need of software development. He is also involved in maintaining legacy software, to ensure availability of the modeling platform to the ITRC and its industrial partners. Roald has a background in engineering, with several years of experience in professional software development for industrial control systems and embedded applications.

Jim W. Hall, the professor of climate and environmental risks at the University of Oxford, is a founder and director of Oxford Infrastructure Analytics Ltd. Jim leads the UK Infrastructure Transitions Research Consortium, which has developed the world’s first national infrastructure simulation models for appraisal of national infrastructure investment and risks. His book, The Future of National Infrastructure: A System of Systems Approach, was published by Cambridge University Press in 2016. He sits on the Expert Advisory Group for the National Infrastructure Commission and Chairs the DAFNI Data and Analytics Facility for National Infrastructure. He is a fellow of the Royal Academy of Engineering and for the last ten years he has been a member of the UK independent Committee on Climate Change Adaptation.

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Abbreviations and Acronyms

AADF Average Annual Daily Freight

AADT Average Annual Daily Traffic

BCR Benefit-Cost Ratio

CAAV Civil Aviation Administration of Vietnam

CBA Cost-Benefit Analysis

CGE Computational General Equilibrium

CI Capital Investments

CMIP5 Coupled Model Intercomparison Project Phase 5

CVTS Commercial Vehicle Tracking System

DMDU Decision-Making Under Deep Uncertainty

DRVN Directorate of Roads for Vietnam

EAD Expected Annual Damages

EAEL Expected Annual Economic Losses

GCM Global Climate Model

GDP Gross Domestic Product

GIS Geospatial Information Systems

GLOFRIS Global Flood Risk with Image Scenarios

GSO General Statistics Office of Vietnam

GVA Gross Value Added

IFPRI International Food Policy Research Institute

IMF International Monetary Fund

IMHEN Vietnam Institute of Meteorology, Hydrology and Climate Change

IO Input-Output

JICA Japan International Cooperation Agency km Kilometers m Meters

MapSPAM Spatial Production Allocation Model (MapSPAM)

MARD Ministry of Agriculture and Rural Development in Vietnam

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MoNRE Ministry of Natural Resources and Environment in Vietnam

MoT Ministry of Transport in Vietnam

MRIA Multi-Regional Impact Assessment

MRIO Multi-Regional Input-Output datasets

MT Metric Tons

NPV Net Present Value

OD Origin-Destination

OIA Oxford Infrastructure Analytics

OSM OpenStreetMap

PDoT Provincial Departments of Transport

RCP Representative Concentration Pathways

TDSI Transport and Strategy Development Institute

TEU Twenty-Foot Equivalent Unit

UN United Nations

UNCTAD UN Conference on Trade and Development

UN COMTRADE United Nations Commodity Trade statistics databases

US$ United States Dollars

VINAMARINE Vietnam Maritime Administration

The Comprehensive Study on the Sustainable Development of Transport VITRANSS II System In Vietnam

VIWA Vietnam Inland Waterway Administration

VND Vietnamese Dong

VRAMS Vietnam Road Asset Management System

WHO World Health Organization

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CURRENCY MEASURE

Currency unit — US$

WEIGHT AND MEASURES

Metric system

BASELINE DATA YEAR Commodity freights — 2009, 2016 Macroeconomic data — 2012 Census data — 2012 Business Survey data — 2012

EXCHANGE RATES US$1 in 2016 = 22,500 VND US$1 in 2012 = 22,000 VND US$1 in 1999 = 14,000 VND

CURRENCY INFLATION US$1 in 2016 = US$1.5 in 1999 US$1 in 2016 = US$1.05 in 2012

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Executive Summary

Vietnam is one of the world’s fastest growing economies—with Gross Domestic Product (GDP) growth above 7 percent in the first quarter of 2018—and strong forecasted growth for the remaining year (World Bank 2018). Vietnam is also projected to be among the fastest growing economies over the 2016 to 2050 period, capable of sustaining a potential 5 percent annual GDP growth rate (PwC 2017). However, this rapid growth is threatened by extreme weather events such as storms, floods, typhoons, and landslides. Vietnam ranks high as a natural disaster hotspot; two or more multihazard events potentially expose 60 percent of the land area and 71 percent of the population to risk (Dilley et al 2005)—which could result in annual average asset losses amounting to 1.5 percent of GDP, and consumption losses amounting to 2 percent of GDP (Hallegatte et al 2016).

Climate change will exacerbate these extreme hazards. The Ministry of Transport (MoT) in Vietnam aims to develop a national strategy for climate resilient transport and plans—as part of the transport sector’s contribution to the Nationally Determined Contributions (NDCs)—to meet the Paris Climate Agreement targets. To this end, this report intends to support MoT on the development of multimodal network-level criticality and vulnerability analysis, as well as methods and tools to inform prioritization of investments in transport asset management to ensure integration of climate and natural risk considerations. This report presents the results from the Transport Multi-Hazard Risk Analysis for Vietnam at two scales—national and provincial—and draws upon policy recommendation to enhance climate resilience of the transport sector. The scope of the national analysis covers key national-scale roads, railways, civil aviation, inland waterways, and maritime systems that make up Vietnam’s multimodal transportation infrastructure. At the national scale, the focus is to understand the economic impacts of disruptions to multimodal freight transportation due to infrastructure failures. The scope of the province-scale analysis covers three specific provinces—Lao Cai, Binh Dinh, and Thanh Hoa—and their road networks only, which are represented in greater granularity than the national-scale analysis. The road networks include national, provincial, district, and commune roads and other assets such as bridges and culverts, among others. At the province scale, the focus is to understand how road failures impact access to key locations within communes, thereby affecting economic output generation. At both national and province scales, two types of natural hazard—flooding and landslides—induce transport failures. The flooding hazards considered in this study include river flooding (i.e., flooding caused by rivers overtopping their banks), surface water flooding (i.e., flooding caused by extreme rainfall—also known as “pluvial” or “flash” flooding), and tidal flooding (i.e., flooding caused by typhoon-induced storm surges). Besides looking at the present situation (the year 2016), we use climate change scenario-driven model outputs representing future flooding in the years 2025, 2030, and 2050. The landslide hazards information includes model outputs for landslide susceptibility presented for current conditions (the year 2016) and future climate change scenarios in 2025 and 2050. The framework developed for this study outlines a system-of-systems methodology. Each transport mode (road, railways, inland waterways, maritime, and airlines) considered in this study is treated as

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an infrastructure system, which is the collection and interconnection of all physical facilities and human systems that operate in a coordinated way to provide infrastructure service (Hall et al 2016). The infrastructure or transport service refers to the mobility for freight and passengers between locations. The multimodal transport infrastructure is then defined as a system-of-systems, which is the collection and interconnection of individual transport systems.

The framework presents the following types of system-of-systems assessments useful for decision- making:

• Criticality assessment as the measure of a transport link’s importance and disruptive impact on the rest of the transport infrastructure (Pant et al 2015). • Vulnerability assessment as the measure of the negative consequences caused by failures of transport links from external shock events (Pant et al 2016). • Risk assessment as the product of the probability of a hazard and the consequences of transport link failures. • Adaptation planning as engineering measures taken to reduce risks. In the context of climate change, the planned adaptation seeks to capitalize on the opportunities associated with climate change (Füssel 2007). Criticalities for the national-scale roads and railway networks are measured in terms of the total economic impact metric, measured in United States dollars (US$) per day as the sum of the macroeconomic losses and the increased freight redistribution costs incurred due to their failures. At the province scale, the study estimates road network criticalities in terms of economic impacts estimated as the sum of the economic losses (net revenue losses) in US$ per day, due to lack of accessible routes toward the commune centers, and the increases in rerouting costs where it is possible to maintain access to the commune centers after the disruptions. The study estimates the risks of extreme hazard failures by combining knowledge of the hazards and impacts into one metric. The network risks are estimated in terms of an Expected Annual Economic Loss (EAEL) metric. The losses signify the daily economic impacts, estimated in the criticality assessment, multiplied by certain duration of disruption during which the affected network link is assumed to be out of operation. The adaptation analysis explores the measures intended to improve the structure reliability of road assets to make them more resistant to climate change impacts. The adaptation options for a particular road asset are quantified in terms of their costs and benefits. The costs include the initial cost of investment to implement the adaptation options, along with the routine and periodic costs of maintaining the climate resilience of the road asset. The benefits include the avoided losses, in terms of the damage (or rehabilitation) costs and the network-wide economic impacts of transport flow disruptions due to the road assets’ failure, were the adaptation option not implemented. Based on the analysis, the key findings and recommendations are summarized as follows. Vietnam’s transport sector needs to prepare for extreme hazards of increasing intensities and with increasing frequency due to climate change. Our analysis shows that under climate change scenarios, exposures to extreme hazards would substantially increase for all transport sectors in Vietnam. The overall maximum kilometers (km) of national roads and railways along with province- scale road networks exposed to extreme hazard levels increases under climate change scenarios. For example, in Lao Cai, road length exposures to extreme landslides change from 142 km in the current

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scenarios to 210 km in the future high-emission scenario for 2050, shown in Representative Concentration Pathway (RCP) 8.5, or RCP8.5. In this scenario, extreme levels of flooding seen in 1,000-year events could start occurring in five-year events, making major inland, maritime, and airports vulnerable to river flooding with increasing frequency, translating to increased risk exposure. Systemic understanding of locations whose failures create increasing economic risks presents a strong economic case for investing in building climate resilience of Vietnam’s transport networks. The model and analysis results highlight systemic criticalities and hazard-specific, high-risk locations in the networks, which can be prioritized for further detailed investigations into climate resilience. The model results in figure E.1 show locations of road networks whose failures can result in very high daily losses of up to US$1.9 million per day, while railway failures can result in losses as high as US$2.6 million per day. When factoring such losses, the wider implications of macroeconomic losses due to transport disruptions become quite crucial. Generally, such effects are often ignored in transport analysis, which results in underestimating the impacts of transport disruptions. Comparisons of current and future hazard risks prominently highlight the strong case for Vietnam to invest in building climate-resilient national-scale roads and railways. Comparisons also strongly indicate that access to economic opportunities in several provincial areas will be severely affected without investment in building climate-resilient roads. The expected annual economic losses by 2030—a measure of risks—due to transport failures from future climate change-driven river flooding significantly increase by more than 100 percent at several locations across national roads, railways, and province road networks. Such increases are recorded at all network links with high impacts. The analysis also shows the three Vietnamese provinces will experience a dramatic increase in risk due to climate change scenarios, up to 400 percent (Lao Cai), 900 percent (Binh Dinh), and 4,000 percent (Thanh Hoa) on road links with the highest losses. Additionally, several new instances of significant losses also emerge in future flooding scenarios for the three provinces.

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Figure E.1. Maximum Economic Losses and Benefit-Cost Ratios of National Road Networks Due to Transport Disruptions

A. Maximum total economic loss B. Maximum BCR of adaptation over time

Vietnam’s road networks require investments to overhaul existing road assets to higher climate- resilient design standards. Though such investments could be costly, their benefits outweigh the costs for priority network assets, making adaptation investments viable options for roads. The analysis has demonstrated that the national-scale and province-scale road networks all need adaptation planning to protect against future climate-related hazards. The analysis shows the Benefit-Cost Ratios (BCR) of adaptation investments into national-scale roads are mostly greater than one. The analysis suggests that, for some national-scale roads, upgrading to climate-resilient designs could cost up to US$3.4 million per kilometer—very high and comparable to costs of constructing high-standard new highways. But the high economic benefits of such investments justify such high costs. When the top 20 road links with highest maximum BCRs are selected, the analysis shows that cumulative climate adaptation investments amount to approximately US$95 million initially, and over

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35 years total approximately US$153 million. The cumulative benefits over 35 years of such investments—estimated by adding the benefits from individual links—are substantial, ranging between US$651 million and US$3.66 billion. When looking at adaptation to flooding, the number of links with BCR > 1 doubles when considering climate change projections. Similarly, for province-scale road networks a relatively small, but significant, number of assets have BCRs > 1. Many of these are bridges and local roads, which are crucial for accessing locations of economic activities. For all road networks, the increasing BCRs under future climate-hazards scenarios strengthen the case for investing in climate resilience to protect again future climate risks. Table E.1 summarizes the results of the adaptation analysis at the provincial level.

Table E.1. Climate Adaptation Analysis for Province-Scale Road Networks

Road network assets Lao Cai Binh Dinh Thanh Hoa

Percent of assets with BCR > 1 15 2–3 2–3

Number of assets with BCR > 1 190 220–330 530–800

Adaptation Investment over 35 years (US$ millions) 12.9 1.6 2.3

Adaptation Benefits over 35 years (US$ millions) 16.4–22.5 14.2–31.4 7.8–22.3

Vietnam’s transport networks can become more resilient if they function as integrated multimodal systems, achieved by improving existing and creating new multimodal linkages. This study has shown economic impacts of disruptions can be reduced by building additional transport multimodal connectivity. Often known as increasing a network’s “redundancy,” this phrase might be interpreted as being wasteful and inefficient. However, providing extra connectivity and capacity will assist the everyday flow of goods and people, as well as providing new opportunities for rerouting when major disruptions occur.

This study shows a very clear benefit of redistributing flows from the road network to the railways and inland and maritime networks. Even a 10 percent shift of road freights, especially to railways, can lead to a 20 to 25 percent reduction of economic impacts. In addition, enhancing railway and waterway efficiency can reduce risks for already congested roads. Moreover, existing unused capacity in the railways accommodates the additional modal shift from roads. The potential railway losses are also significantly reduced through the availability of multimodal options, which should be considered in the future.

It would be important to invest in improving data gaps in future studies in order to make more robust economic case for investment in the transport network. Vietnam’s transport resilience planning can benefit from a system-of-systems transport risk analysis. Importantly, creating cross- sector and cross-organization collaborations helps increase capacity to undertake further studies and pursue continued efforts to improve the underlying datasets. The study has dealt with severely

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limited data, highlighted throughout the report. Some of these limitations exist in assembling and standardizing:

• Topological representations of infrastructure networks with proper attributes • Transport flows that represent latest conditions and trends • Transportation cost assignments reflective of existing conditions • Economic flows data showing current structure of the country and regional economies • Probabilistic hazards at similar spatial resolutions and with Vietnam-specific climate scenarios • Seasonality information of extreme levels of hazards and transport network flows • Information on disruption durations and response behaviors • Costs of various adaptation options most relevant to Vietnam.

Next steps: Creating a cross-sector and cross-organization collaboration involving various government agencies is extremely important for increasing capacity to standardize and share data. While the model developed in this study can be readily adapted to accommodate further improvements, this study addresses only some of the economic and social functions of transport infrastructure. Notably, it has not explored the role of transport infrastructure in enabling passenger travel for work or other purposes, or for labor market participation. That should be included in future studies, alongside other wider economic and social benefits of transport infrastructure. In these recommendations we have already identified that prioritizing investments to improve transport network resilience would require consideration of the costs and effectiveness of alternative interventions. Building on this study of transport network vulnerability and risks, we recommend studying the costs and effectiveness of alternative interventions as the next step.

References

Dilley, Maxx, Robert S. Chen, Uwe Deichmann, Arthur L. Lerner-Lam, Margaret Arnold, Jonathan Agwe, Piet Buys, Oddvar Kjevstad, Bradfield Lyon, and Gregory Yetman. 2005. Natural Disaster Hotspots: A Global Risk Analysis. Disaster Risk Analysis Series. Washington, DC: World Bank. http://documents.worldbank.org/curated/en/621711468175150317.

Füssel, Hans-Martin. 2007. “Adaptation Planning for Climate Change: Concepts, Assessment Approaches, and Key Lessons.” Sustainability Science 2 (2): 265–75. doi: 10.1007/s11625-007- 0032-y.

Hall, Jim W., Martino Tran, Adrian. J. Hickford, and Robert J. Nicholls, eds. 2016. The Future of National Infrastructure: A System-of-Systems Approach. Cambridge, UK: Cambridge University Press.

Hallegatte, Stephane, Adrien Vogt-Schilb, Mook Bangalore, and Julie Rozenberg. 2016. Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters. Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/25335.

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Pant, Raghav, Jim W. Hall, and Simon P. Blainey. 2016. “Vulnerability Assessment Framework for Interdependent Critical Infrastructures: Case Study for Great Britain’s Rail Network.” European Journal of Transport and Infrastructure Research 16 (1): 174–94. https://eprints.soton.ac.uk/385442/.

Pant, Raghav, Jim W. Hall, Simon. P. Blainey, and John M. Preston. 2015. “Assessing Single Point Criticality of Multi-Modal Transport Networks at the National-Scale.” Paper presented at the 25th European Safety and Reliability Conference, Zurich, Switzerland, September 2015. https://eprints.soton.ac.uk/385456/.

PwC (PricewaterhouseCoopers). 2017. The World in 2050—The Long View: How Will the Global Economic Order Change by 2050. PwC Economics & Policy Team report. London: PricewaterhouseCoopers LLP. https://www.pwc.com/gx/en/world-2050/assets/pwc-the-world- in-2050-full-report-feb-2017.pdf.

World Bank. 2018. “Vietnam’s economic prospect improves further with GDP projected to expand by 68 percent in 2018.” Press Release, June 14. https://www.worldbank.org/en/news/press- release/2018/06/14/vietnams-economic-prospect-improves-further-with-gdp-projected-to- expand-by-68-percent-in-2018.

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Chapter 1: Introduction

Background and Objectives The Socialist Republic of Vietnam (henceforth referred to as Vietnam) is one of the world’s fastest growing economies with Gross Domestic Product (GDP) growth above 7 percent in the first quarter of 2018, with strong forecasted growth for the remaining year (World Bank 2018). This follows from a consistent average GDP growth of 6.4 percent since the 2000s.1 Vietnam is also projected to be among the fastest growing economies over the 2016 to 2050 period, capable of sustaining a potential 5 percent annual GDP growth rate (PwC 2017). However, such rapid growth is threatened by extreme weather hazards (storms, floods, typhoons, landslides). Previous reports suggest Vietnam already ranks high as a natural disaster hotspot of two or more multi-hazard events, potentially exposing 60 percent land area and 71 percent population to risk (Dilley et al 2005)—which could result in annual average asset losses amounting to 1.5 percent of GDP and consumption losses amounting to 2 percent of GDP (Hallegatte et al 2016). It is possible that climate change will exacerbate these extreme hazards, even after factoring uncertainties in downscaled global climate model predictions (MoNRE 2009; World Bank 2011; Irish Aid 2017). Recognizing that the country’s vulnerability to impacts of climate change can impede its growth, and as part of its Nationally Determined Contributions (NDCs) to meet the Paris Climate Agreement targets, the Ministry of Transport (MoT) in Vietnam aims to establish national transport strategies and plans as part of the transport sector’s contribution to deliver on NDC implementation. To this end, this report supports MoT in the development of multimodal network level criticality and vulnerability analysis, as well as methods and tools to inform prioritization of investments in transport asset management to ensure integration of climate and natural risk considerations. In delivering the multimodal transport network criticality and vulnerability analysis to prioritize maintenance, rehabilitation, and infrastructure upgrading, this study answers some key questions relevant to transport planners, investors, and relevant stakeholders, namely: 1. Where and what are the key transport network locations exposed to different types of extreme natural hazards? 2. In which areas in the country are transport assets particularly exposed to hazards, as predicted by model outputs of current and future climate change-driven natural hazard scenarios? 3. What are the wider macroeconomic losses at the national scale when freight transport is disrupted due to transport failures? 4. What are the impacts on the local commune-scale economies due to lack of access to key locations of economic activity, because of local road failures? 5. What are the impacts of transport disruptions in terms of increased transportation costs of rerouting traffic for maintaining service continuity?

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6. How resilient are the multimodal transport networks in providing continuous service when individual transport modes are damaged or disrupted due to external natural hazard shocks? 7. When considering the key climate resilience investments and strategies evaluated for current and future climate scenarios, what are the net benefits of adaptation? 8. Where and what are the key transport network locations prioritized according to highest net benefits of climate adaptation measures? 9. What are the most robust climate resilience interventions or policies to reduce the vulnerability of critical segments to future climate change impacts, taking account of the uncertainties and sensitivities?

In answering the above questions, some specific objectives satisfied in this report include the following: 1. Creating multi-scale geospatial network flow models to represent the multimodal transport infrastructures in Vietnam, both in the present and future 2. Modeling traffic flow disruptions and flow reallocations when network assets fail due to natural disasters 3. Evaluating the potential economic and social impacts when the multimodal transport networks are disrupted by natural disasters 4. Creating network criticality metrics to systemically measure and identify critical segments of the multimodal transport networks that lack or have built-in resilience and/or redundancy 5. Performing exhaustive scenario-based simulations to incorporate multiple uncertainties in underlying model assumptions. This aligns with the decision making under deep uncertainty (DMDU) approaches (Espinet et al 2015) 6. Incorporating various climate-resilient transport investments and policies to evaluate the adaptation options and identify robust interventions or policies to reduce the vulnerability of critical segments to future climate change impacts, taking account of the uncertainties and sensitivities.

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Scope of the Study This report focuses its recommendations at two scales: national and provincial. The focus area of this study is the mainland of Vietnam,2 which on the west and north shares land borders with neighboring countries (Lao PDR, Cambodia, and China) accessible by road, and is surrounded by coastal lines on the east and south. For the national-scale analysis, input data is derived from provincial, district, or commune-level statistics where appropriate. The province-scale analysis focuses on three specific provinces—Lao Cai, Binh Dinh, and Thanh Hoa—where information is derived from commune-level statistics.

Figure 1.1 provides a representation of the regional and international boundaries of Vietnam relevant to this study. The three provinces of Lao Cai, Binh Dinh, and Thanh Hoa, for which the detailed road network analysis is conducted, are highlighted in dark gray.

The scope of the national analysis covers the key national-scale roads, railways, airline, inland waterways, and maritime systems that make up Vietnam’s multimodal transportation infrastructure. At this scale the study focuses on understanding the economic impacts of disruptions to freight transportation due to transport failures. The scope of the provincial analysis is confined to road networks only, which are represented in greater granularity than the national-scale roads and include national, provincial, district, and commune roads and other assets such as bridges and culverts, among others. At this scale, the study focuses on understanding how road failures impact access to key locations within communes, thereby affecting economic output generation. Specifically, for all road networks considered in this study, a detailed adaptation analysis model is created to quantify the benefits and costs of adaptation, identify the road network assets where climate-resilient investments should be prioritized, and provide estimates of the scales of investments required. An overview of all infrastructure related datasets used in this study is given in appendix B of this report. At both national and provincial scales, transport failures are induced by two types of natural hazards: flooding and landslides. The flood hazards considered in this study are river flooding (i.e., flooding caused by rivers overtopping their banks), surface water flooding (i.e., flooding caused by extreme rainfall—also known as “pluvial” or “flash” flooding) and tidal flooding (i.e., flooding caused by typhoon-induced storm surges). Besides looking at the present situation (the year 2016), we use climate change scenario-driven model outputs representing future flooding in the years 2025, 2030, and 2050. The landslide hazards information includes model outputs for subsets of landslides quantified in terms of landslide susceptibility presented for current conditions (the year 2016) and future climate change scenarios in 2025 and 2050. Chapter 2, section “Natural Hazard Exposure of Transport Networks” and appendix B provide further details for flood hazard data.3

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Figure 1.1. Vietnamese Provinces and Neighboring Countries Considered in the Study

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Study Contributions and Limitations The main contribution of this study is to provide detailed spatial evidence of systemic vulnerabilities and risks on the multimodal transport systems of Vietnam. Several study contributions would be valuable to stakeholders in Vietnam such as Ministry of Transport (MoT), Directorate of Roads for Vietnam (DRVN), Provincial Departments of Transport (PDoT), Civil Aviation Administration of Vietnam (CAAV), Vietnam Maritime Administration (VINAMARINE), Vietnam Inland Waterway Administration (VIWA), Transport Development and Strategy Institute (TDSI), Ministry of Agriculture and Rural Development (MARD), and Ministry of Natural Resources and Environment (MoNRE).

This study includes the following specific useful contributions: 1) Creating unique datasets and modeling resources a. First-of-a-kind representations of topologically connected geospatial national-scale roads, railways, inland waterways, and maritime multimodal transport networks with flow assignments for Vietnam b. Detailed agriculture crops and commodity or industry level network flows mapped onto the multimodal transport networks increase understanding of the domestic freight flow patterns in the country c. Detailed transport cost estimates increase understanding of realistic criteria for transport freight flow assignments d. Long-term growth forecasts incorporated onto the geospatial networks e. The study’s codebase, developed in Python programming language as an open-source resource, is available to the public at https://github.com/oi-analytics/vietnam-transport. The methodology behind the code has been documented in detail through this report, and a separate user document on the compilation and creation of underlying data and code is also available publicly at: https://vietnam-transport-risk- analysis.readthedocs.io/en/latest/. f. Testing of the underlying codes to perform billions of computations of network flow assignments and failure scenarios with optimal performance, a fundamental requirement for a study of this scale. 2) Prioritizing criticality-based networks a. The key metrics created and analyzed in this study answer the previously highlighted questions relevant to transport stakeholders, who want to know the effects of transport disruptions on continued provision of service and transport costs. b. The detailed network assessment conducted in this study helps identify locations most important for the continuity of the transport service in the country, and whose failures can potentially lead to large-scale socio-economic consequences. c. The study’s ranking of critical assets, based on their disruptive potential, provides a means to prioritize and sequence risk reduction activities. d. Ranking of critical assets further supports the rationale for prioritizing investments to reduce vulnerability and build systemic resilience, forming the basis for long-term adaptation planning (Thacker et al 2018).

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3) Increasing the understanding of hazard vulnerability a. The study spatially depicts the exposure of transport networks to various natural hazards at detailed spatial scales, enabling understanding of the potential severity of different threats to infrastructure networks. b. Study results presented at national and provincial scales highlight areas where several transport assets are more prone to be exposed to hazards in the current scenarios and how this might change in the future due to climate change driven scenarios. c. The study’s spatial information on extreme hazard exposures of transport networks can inform national and provincial transport planning decisions to allocate adequate budget for short-term and long-term transport risk reductions. 4) Developing a roadmap for adaptation planning a. A key contribution of this study is to provide the tools to undertake adaptation analysis whereby the benefits and costs of different strategies designed to build transport structural resilience can be assessed under various hazard scenarios. b. The study presents various adaptation options at the scale of individual road assets, as well as the cost-benefit analyses metrics to deliver a detailed understanding of investment options for each asset under different hazard scenarios. c. The study performs key analysis to understand whether asset level adaptation planning should be done proactively to protect against future climate hazards. d. The study catalogues and presents locations of all road assets with high benefits of adaptation to provide decision-makers with information that will help prioritize resources toward the most critically important assets in the networks. 5) Assessing multimodality a. The study’s presentation of key methods and analyses quantifies the effects of transport failures, if multimodal options were available. b. As one of its key aims, the study’s multimodality assessment provides evidence supporting the need for enhanced multimodal connectivity as an adaptation strategy. c. Understanding the benefits of multimodality at detailed geospatial network scales provides a means to target network locations where multimodal options could be strengthened through more investments. 6) Assessing uncertainty a. The models and analyses presented in this report have many sources of uncertainties coming from lack of proper data on physical transport network structures and their usage statistics, among others. These uncertainties have been accounted for throughout the analysis. b. The study follows a robust decision-making approach (Lempert et al 2013) whereby the analysis quantifies the extreme ranges (minimum and maximum) of potential flows, criticalities and adaptation. This approach provides a decision-making tool that accounts for robust performance of different adaptation options under different scenarios.

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The analysis presented in this report offers a high-level indicative assessment of transport systems and their natural hazard risks, providing a first-order screening whereby locations and assets with high risks can be narrowed down for further investigation. Hence, certain considerations should be made in interpreting this work. Nevertheless, most of these limitations exist due to lack of proper data; with improvement in input data the underlying model is fully capable of correcting several limitations underlined below: 1) Physical infrastructure network data and representations a. The transport data created in the model serves as a network presentation of functional connectivity of the transport system, rather than a detailed master plan representation of each location’s detailed spatial layout and attributes. For example, in this analysis, Ho Chi Minh port is represented as a point in space, while in reality it exists over a large area. b. Similarly, the represented road, railways, airports, ports, and multimodal links show general travel routes. Any details provided for the physical width, number of lanes or lines, or navigational channels of these systems have been estimated. c. The representation of the networks’ built-in functional and physical connectivity is also heavily contingent of the availability or lack of data. For example, if the available data does not show a road or rail connection that exists, the model will not be able to represent that connection. d. The multimodal links in this analysis represent derived connectivity, with as many links as possible verified through satellite imagery. 2) Network flow assignments a. As explained later in the report, the model estimates network flow assignments from several sources and datasets. Several assumptions have been taken in interpreting the flows. b. Representation of future flows and losses are estimated based on high-level forecasts of growth for the whole country. c. Hence, the flow and failure values in the analysis show the high-level trends, rather than the exact estimates of actual flows. 3) Failure and hazard vulnerability analysis a. Gridded datasets show hazards at different spatial resolutions, which are sometimes very coarse. Thus, the hazard information should not be used for detailed site-specific analysis. b. Derived from the analysis of transport network exposures to hazards, the main insights provide useful overviews of the likely hazards around specific locations. 4) Adaptation analysis a. The adaptation analysis presented in this report reflects the underlying data of cost estimates for different options under consideration. Therefore, the cost estimates derived from datasets represent best estimates. b. The main insights from the adaptation analyses provide a means to understand how different options can be evaluated and which scenarios should be considered.

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Report Structure Following this introductory chapter, the remainder of the report is structured as follows: Chapter 2 — Vietnam Profile: Transport Network and Exposure to Natural Hazard: Chapter 2 describes the underlying transport network information assembled and created in this study, the types of hazards considered—with their source—climate change scenarios (if considered), spatial resolutions, and spatial coverage. The chapter infers some information regarding the state of the infrastructure from the underlying network data, which is compared with relevant literature wherever possible.

Chapter 3 — Vulnerability, Criticality and Risk Assessment: Chapter 3 presents the results for the transport criticality assessment of freight flow disruptions on the assembled transport networks in Vietnam. The criticality assessment considers an exhaustive set of individual network link scenarios exposed to hazards for failure. In addition, the assessment considers impacts in terms of metrics that give a sense of the network locations with the most socio-economic impacts. A risk assessment to infer the hazards that cause the impacts follows. The ranges of disruptive impacts across each hazard are quantified for each network to better understand whether hazard impacts will increase due to future climate scenarios. Chapter 4 — Adaptation Strategy and Analysis: Chapter 4 presents the adaptation options considered for national-scale and province-scale road network assets, with a detailed benefit-cost analysis performed for each asset under different hazard scenarios. In addition, the chapter evaluates the ranges of adaptation options and their benefits, exploring the advantages of adapting to future climate scenarios. Chapter 5 — Exploring Multimodal Options for National-Scale Transport: This chapter explains the development of the study’s multimodal transport system. Following this, the chapter evaluates the effect of multimodality on failures by exploring modal shifts from road and rail networks. Chapter 6 — Conclusions and Policy Recommendations: Chapter 6 presents recommendations for building and enhancing transport resilience to climate change impacts.

Notes

1. See the World Bank’s online country page for Vietnam: http://www.worldbank.org/en/country/vietnam/overview.

2. For the purposes of this study, the analysis was confined within the mainland of Vietnam, considering the data availability and significance of traffic flows.

3. The natural hazard datasets used in this study have been obtained from third party sources—either from the Government of Vietnam or through the World Bank partners—and have not been altered in any way by the authors.

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References

Espinet, Xavier, Amy Schweikert, and Paul Chinowsky. 2015. “Robust Prioritization Framework for Transport.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 3 (1). doi: 10.1061/AJRUA6.0000852.

Irish Aid. 2017. Vietnam Climate Action Report for 2016. Irish Aid Resilience and Economic Inclusion Team report, Limerick, Ireland: Irish Aid. https://www.irishaid.ie/media/irishaid/allwebsitemedia/30whatwedo/climatechange/Vietnam- Country-Climate-Action-Reports-2016.pdf.

Lempert, Robert, Nidhi Kalra, Suzanne Peyraud, Zhimin Mao, Sinh Bach Tan, Dean Cira, and Alexander Lotsch. 2013. “Ensuring Robust Flood Risk Management in Ho Chi Minh City.” Policy Research Working Paper 6465, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/749751468322130916.

MoNRE (Ministry of Natural Resources and Environment). 2009. Climate Change, Sea Level Rise scenarios for Vietnam. Hanoi: Ministry of Natural Resources and Environment, Vietnam. https://www.preventionweb.net/files/11348_ClimateChangeSeaLevelScenariosforVi.pdf.

Thacker, Scott, Scott Kelly, Raghav Pant, and Jim W. Hall. 2018. “Evaluating the Benefits of Adaptation of Critical Infrastructures to Hydrometeorological Risks.” Risk Analysis 38(1): 134–50. doi: 10.1111/risa.12839.

World Bank. 2011. Climate Resilient Development in Vietnam: Strategic Directions for The World Bank. Report. World Bank Sustainable Development Department, Vietnam Country Office, Vietnam. http://documents.worldbank.org/curated/en/348491468128389806.

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Chapter 2: Vietnam Profile: Transport Network and Exposure to Natural Hazard

Transport Networks This section describes the current networks with freight flow volumes and costs across national-scale roads, railways, inland waterways, maritime ports, and airline networks. Similarly, flows are also identified on province roads networks in terms of the access to important commune centers within provinces, uncertainties of flows due to rice-crop seasonality, and network cost assignments are considered.

National-scale road infrastructure networks The national-scale network model created for the study extends to about 30,900 km and consists of 2,130 nodes and 2,512 links. Of the 30,900 km of the network analyzed, 30,032 km or 97.2 percent, is paved; a mere 868 km remains unpaved. Roads have been classified in scale of 1 to 6, with a greater traffic volume associated with higher road class (table 2.1). Table 2.2 presents the estimated classification.

Table 2.1. Road Categories by Average Daily Vehicle Traffic Counts

Road traffic count Road category

> 6000 1

3000 – 6000 2

1,000 – 3000 3

300 – 1,000 4

50 – 300 5

<= 50 6

Source: MoT Vietnam 2000.

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Table 2.2. Estimated Total Lengths by Road Category in the National-Scale Road Network Model for Vietnam

Road class Length (km) Length (%)

1 1,656 5.35%

2 2,331 7.54%

3 4,951 16.02%

4 6,449 20.87%

5 8,561 27.70%

6 6,952 22.70%

Source: Authors’ estimation using CVTS GPS data mapping

Province-scale roads networks

Given the underlying geospatial datasets had the same attributes, the province-scale network models used similar types of assumptions across all provinces. Table 2.3 shows the estimated lengths of paved and unpaved roads of different levels in each of the province road network models. The estimates indicate these provinces contain mostly unpaved roads.

Table 2.3. Estimated Lengths by Road Category in the Province-Scale Road Network Model for Vietnam

Paved Unpaved Paved Unpaved Province Nodes Links Road level (km) (km) (%) (%)

National 579 0 14.71% 0.00% Provincial 435 0 11.06% 0.00% Local (district/ Lao Cai 3,744 4,697 65 1,342 1.65% 34.11% commune) Other 0 1,514 0.00% 38.48% Total 1,079 2,857 27.42% 72.58% National 341 0 6.31% 0.00% Provincial 321 0 5.93% 0.00% Local (district/ Binh Dinh 21,686 26,213 226 446 4.17% 8.25% commune) Other 0 4,075 0.00% 75.34% Total 887 4,522 16.41% 83.59% National 921 0 4.58% 0.00% Provincial 457 0 2.27% 0.00% Local (district/ Thanh Hoa 66,863 91,304 394 2,312 1.96% 11.49% commune) Other 5 16,025 0.03% 79.67% Total 1,777 18,337 8.83% 91.17%

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Railway infrastructure network The rail infrastructure network model was derived from data on station point locations and rail line geometries provided from the Ministry of Transport (MoT) dataset, including the VITRANSS II datasets from 2009 (JICA and MoT 2010). The resulting network contains 313 nodes, of which 234 are stations and 324 are links. The modeled length of the rail network is approximately 2,662 kilometers. Table 2.4 reports the lengths of some main routes. The main hub of the rail network lies around Hanoi, with the Hanoi to Ho Chi Minh route as the longest line of the network by far.

Due to an aging railway infrastructure largely comprised of a single-track system, railway usage is very limited in Vietnam, which limits train lengths in the difficult and narrow terrains along routes (Systra 2018).

Table 2.4. Estimated Total Link Lengths and Percentages of Rail Routes Identified in the National- Scale Rail Network Model for Vietnam

Rail route code Route description Length (km) Length (%)

DSTN Hanoi – Ho Chi Minh 1,719 64.56% YV-LC Hanoi – Lao Cai 297 11.16% HN-DD Hanoi – Lang Son 171 6.42% GL-HP Hanoi – Hai Phong 106 3.98% K-HL Bac Giang – Quang Ninh 104 3.90% DA-QT Hanoi – Thai Nguyen 58 2.16% K-LX Bac Giang – Thai Nguyen 55 2.07% BH-VD Hanoi 39 1.47% MP-LD Lang Son 31 1.17% CG-ND Nghe An 30 1.12% CL-PL Hai Duong 17 0.65% MM-PT Binh Thuan 12 0.45% PL-KK Ha Nam 9 0.34% DT-QN Binh Dinh 9 0.34% L-TM Unknown 6 0.22%

Inland waterway infrastructure network The study identified inland waterway network model nodes from data obtained from the Vietnam Inland Waterway Administration (VIWA)1 and the VITRANSS II study. The resulting network contains 131 inland waterway port nodes and 502 links. The modeled length of the inland waterway network is approximately 5,407 kilometers, concentrated along the Mekong Delta in the south, or the Red River Delta in the north, with a very small section in Quang Binh.

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Maritime infrastructure network The study identified maritime network model nodes from geospatial port location data obtained from the MoT, and maritime routes published by the Ministry of Natural Resources and Environment (MoNRE). The resulting network contained 45 maritime ports and 206 links. The modeled length of the maritime network is approximately 5,254 kilometers, for domestic transportation uses only. The Vietnam Maritime Administration (VINAMARINE) classifies international hubs classified as Class 1A ports, and domestic hubs as Class 1 ports (MoT 2015).

Air transport infrastructure network The study identified air transport infrastructure network model nodes from geospatial airport location data obtained from the MoT. Based on a previous MoT study of the VITRANSS II model (JICA and MoT 2010), the study identified main airports with any freight flows between them and created straight-line link geometries to join the paired airports. The resulting network contains 23 airport nodes, of which 8 are connected via 10 links. While the share of air transport of total cargo movement in Vietnam is increasing rapidly, detailed data for air freight volumes were not available at the time of this study. Given this limitation, the study analyzed the air transport sector as a standalone sector, rather than as a multimodal transport option in Vietnam.

Modeling Freight Flow on Transport Networks This study modeled and estimated the following transport flow metrics:

• Average annual daily freight volumes (AADF): The estimated volume of freight in tons per day on an average day between different network locations; and • Net revenue measures: The estimated daily net revenue of goods and services with access to an important location via a transport network. These measures are estimated to understand local road access to important locations at province scale. Traffic flows were constructed based on available existing data, including information received from relevant ministries and departments in Vietnam, data compiled in previous studies and reports on transport planning and development in Vietnam, and open-source data and online information of transport estimates. The study relied on previous origin-destination (OD) matrix provided by MoT, CVTS data from 2017, input-output (IO) matrix, traffic count data from DRVN, crop production data from IFPRI (Koo et al 2018), and other socio-economic statistics, among others. The process of assigning freight transport flows is described in detail in appendix A.

Results of freight flow mapping at national scale The results of the flow assignments provide insights into the following important questions:

• Where are the locations on the network links with the most flow concentrations? • Which links and routes have the largest concentrations of economic values? First the results of the national-scale OD matrix are mapped in figure 2.1 and shown in table 2.5, which presents the different daily tonnages for each commodity considered in the study and split of these daily tonnages by each transport mode (road, railway, air, inland waterways, and maritime). For each commodity, the mode with the highest percentage share is highlighted (in gray) in table 2.5. The result shows the following:

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• The fluctuations in the rice volumes show a significant change in the daily tonnage volumes on the transport systems, which can have huge implications on the values of failure estimates. • Inland waterways and roads are the two most dominant transport modes for these commodities. Depending upon the fluctuations in the rice transport tonnages, the mode shares between these two transport modes varies between 44 and 48 percent. • Maritime transport is the next significant mode, with around 4 to 5 percent modal shares, followed by railways, with a 1.9 to 2.1 percent modal share. Airline transport is almost insignificant. • For commodities such as cement, coal, construction materials, fertilizer, fishery, and sugar, inland waterways represent the most dominant mode. Roads rank as the dominant mode for manufacturing, steel, meat, petroleum, and all crop commodities used in this study. The tonnage volumes and modal shares presented are a subset of the estimated annual tonnages that focus on major national inter-provincial commodity flows and are based on the best available origin-destination data of some commodities. This results in modal share figures that are somewhat different from those from the statistics of the General Statistics Office (GSO) of Vietnam.2 According to the GSO figures for 2015, roughly 76.5 percent of trade flows occurred on road, 17.5 percent on inland waterways, 5.3 percent on maritime, 0.6 percent on railways, and 0.02 percent on airline networks. Hence, the estimated mode shares in this study under-represent the dominance of roads, while over-representing the contributions of inland waterways and railways. The maps in figure 2.1 show the how daily transport flows in Vietnam happen at long distances, connecting the region of economic activities in the south (Mekong Delta) to those in the north (Red River Delta). For the national-scale road network, the high-volume freight routes run through the national highways and expressways on the eastern coast. The railway flows are more concentrated toward the northern sections. The inland waterways have key high-volume routes in the north and south, while the cross-country domestic maritime are the busiest routes. Based on the available data for airlines, the connectivity between the Hanoi and Ho Chi Minh airports is most important for freight transport.

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Table 2.5. Daily Traffic Volumes by Commodity and Transport Mode 57 413 208 Total 3,008 6,382 2,040 2,608 1,746 8,066 41,158 19,315 20,911 69,987 45,509 43,389 23,110 111,640 110,879 465,241 185,828 606,144 103,653 1,265,147 1,767,638 share 9.00% 0.41% 7.49% 2.88% 1.21% 0.03% 0.29% 4.36% 8.12% 0.29% 0.03% 0.60% 6.32% 1.73% 1.80% 3.55% 5.25% 3.00% 4.61% 4.93% 11.55% 13.31% 16.02% 16.01% Percent Percent Maritme 0 9 1 1 16 33 234 912 518 232 751 145 820 1,904 9,977 1,185 4,425 7,284 3,114 12,898 13,921 31,799 58,380 87,065 Tons/day share 7.50% 0.00% 0.07% 0.70% 4.68% 1.07% 0.00% 0.07% 0.01% 54.31% 74.41% 75.68% 65.74% 61.67% 22.15% 20.03% 11.65% 15.76% 60.06% 48.51% 33.78% 24.85% 48.41% 44.78% Percent Percent 0 2 4 0 2 0 146 299 387 5,301 6,840 4,844 Inland waterways 60,626 83,917 27,058 11,912 13,933 14,019 11,211 25,762 346,189 204,781 612,452 791,471 Tons/day share 0.00% 0.00% 0.00% 0.01% 0.00% 0.08% 0.00% 0.00% 0.00% 0.18% 0.00% 0.00% 0.00% 0.35% 0.03% 0.07% 0.02% 0.01% 0.00% 0.01% 0.03% 0.01% 0.02% 0.02% Percent Percent Air 1 5 0 3 1 0 0 1 0 0 0 6 0 8 2 0 7 9 12 48 43 152 254 287 Tons/day share 3.21% 1.81% 1.82% 4.79% 0.16% 2.53% 0.37% 0.03% 2.09% 3.11% 0.71% 0.33% 0.76% 4.98% 6.20% 0.79% 0.84% 5.42% 0.16% 1.40% 1.33% 1.17% 2.08% 1.88% Percent Percent Railway 0 1 3 7 30 20 87 13 13 436 199 555 384 325 3,581 8,423 2,022 1,973 4,702 2,350 8,043 1,210 26,332 33,165 Tons/day share 30.93% 23.37% 13.49% 26.58% 36.96% 82.40% 99.61% 99.61% 92.85% 83.90% 97.94% 99.64% 98.57% 59.22% 77.75% 72.79% 71.49% 77.08% 37.98% 46.51% 59.64% 70.96% 44.87% 48.41% Percent Percent Road 57 404 162 7,138 2,996 5,354 2,033 2,570 1,034 3,064 34,534 14,963 10,940 19,416 50,940 32,532 33,446 10,748 73,557 108,721 153,121 361,478 567,729 855,650 Tons/day Total tons (min) tons Total Commodity Total tons (max) tons Total Cement Coal Constructon Constructon materials Fertlizer Cofee Arabica Cofee Fishery Manufacturing Cashew Cassava Maize Pepper Cofee robusta Cofee Rubber potatoes Sweet Teas Meat Petroleum Steel Sugar Wood Rice (min) Rice (max)

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Figure 2.1. Maximum AADF for the National-Scale Transport Network

A. Total tonnage (Road) B. Total tonnage C. Total tonnage (Inland waterway) (Maritme)

D. Total tonnage E. Total tonnage (Railway) (Air)

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Results of province-scale flow mapping Traffic flows and revenues on province-scale road networks have been calculated based on the IFPRI crops data (Koo et al 2018), and commune-level revenue statistics from GSO.3 Figure 2.2 (Lao Cai), figure 2.3 (Binh Dinh), and figure 2.4 (Thanh Hoa) show heat maps of flow concentrations due to maximum daily crop tonnages (panel A) and maximum daily net revenues (panel B), in US$ thousands per day) being directed toward commune centers of the respective provinces.

The results predominantly highlight the effect of road density in the provinces. For Lao Cai, where the road network is not very dense, some road clusters (e.g., in the Bao Yen district) have more significant usage than others. On the other hand, for Thanh Hoa the road network is very dense, with many route options, making the flows patterns more distributive with few clusters of very significant roads. The road network is sparse in parts of Binh Dinh, which creates more significant clusters in locations such as Lao Cai, while fewer clusters exist in areas where the network is dense, as in Thanh Hoa. The analysis by commodity highlights the net revenue’s dependence on agriculture productivity:

• For Lao Cai, the non-agriculture firm revenues dominate the minimum and maximum net revenue distributions on the network, which result in little variations in flow patterns. • In Binh Dinh, the dependence on agriculture production results in some differences between the minimum and maximum net revenue allocations. • In Thanh Hoa, again, the amount of agriculture production affects the net revenue.

Figure 2.2. Maximum Crop Flows toward Nearest Commune Centers and Maximum Net Revenues on Lao Cai Provincial Roads

A. Maximum tons B. Maximum net revenue of crop flows in Lao Cai of crop flows in Lao Cai

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Figure 2.3. Maximum Crop Flows toward Nearest Commune Centers and Maximum Net Revenues on Binh Dinh Provincial Roads

A. Maximum tons of crop flows in Binh Dinh B. Maximum net revenue of crop flows in Binh Dinh

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Figure 2.4. Maximum Crop Flows toward Nearest Commune Centers and Maximum Net Revenues on Thanh Hoa Provincial Roads

A. Maximum tons of crop flows B. Maximum net revenue of crop flows in Thanh Hoa in Thanh Hoa

Uncertainties in freight flow mapping The national-scale results show the maximum estimates of flow under the following assumptions:

• Estimated from the high-level OD matrices, daily OD tonnages represent the highest daily tonnages allocated on the transport links. • Based on the highest generalized costs of the flows, flow assignments depend on travel speeds and transport tariffs. Considering the range of OD tonnage, this report captures and presents the uncertainties in estimated flows. Figures 2.5A through 2.5D show the ranges of daily freight flow estimates along network links for the national road, railway, inland waterway, and maritime sectors. The results illustrate the flows, ranked from lowest to highest, based on the maximum estimated flows along links. Accordingly, the x-axis is labeled as “percentile rank,” where the percentile rank of 80 implies that 80 percent of links have flows lower than that link. Each of these figures gives a sense of the uncertainties of flow estimates along links. These uncertainties are driven mainly by two factors: a) fluctuations in OD estimates, which depend upon the seasonality of production and transport; and b) changes in route choice due to fluctuating generalized transport costs, which depend upon speeds and transport tariffs. The relative contributions of these factors toward flow uncertainties can be measured by comparing the minimum and maximum flow assignment routes for the same OD pairs. If the minimum and maximum flow assignment routes are the same, then flow assignments are only sensitive to the volumes of OD tonnages. If the minimum and maximum flow assignment routes differ, then the flow assignments are sensitive to the generalized costs of transports.

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The analysis for the national-scale road network shows a 28 percent difference between the minimum and maximum flows for the same OD pairs, suggesting 28 percent of the flow is sensitive to the transport costs. In figure 2.5A, some links show significant variability between minimum and maximum average annual daily freight (AADF) flows, resulting in some noticeable spikes in the plot. Analysis shows small differences in the railway, inland waterway, and maritime network flows, signifying that the flow uncertainties in figures 2.5B through 2.5D are driven by changing OD tonnages rather than changes in the transport costs assigned to these networks. This is explained by the greater variability in attributes of the road network, such as roads types, terrains, speeds, and tariffs, which leads to greater sensitive of traffic flows.

Figure 2.5. Ranges of AADF Flows by Mode

A. Range of AADF fows on road links B. Range of AADF fows on railway links

C. Range of AADF fows on inland waterway links D. Range of AADF fows on maritme links

Similar to the national-scale network, flow assignments on the province-scale networks are also uncertain due to changing values of net revenues generated within communes, and the changing transport costs on roads used to gain access to the nearest commune centers. Figures 2.6A through 2.6C show the ranges of ranked net revenue flow estimated along network links for the province- scale road networks of Lao Cai, Binh Dinh, and Thanh Hoa. When compared to other network links, most road links in each province produce lower values of assigned net revenue flows, due to the concentration of economic activities near a handful of communes with limited access to road routes.

The analysis shows small differences between the chosen routes in the minimum and maximum net revenue flow assignments between villages, crop production sites, and the commune centers. For Lao Cai, only 1.6 percent of the minimum and maximum flow assignment routes differ, with a 2.6 percent difference for Binh Dinh, and a difference of 3.3 percent for Thanh Hoa. Thus, the changing transport costs have very little influence on the transport patterns and the resulting uncertainties in the values of net revenue flows along links, due mainly to most commutes between origins (villages and crop production sites) and destinations (communes) covering small distances along local networks with few alternative routes.

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Figure 2.6. Ranges of Net Revenue Flows Assigned to Road Networks in Lao Cai, Binh Dinh, and Thanh Hoa

A. Net revenue fows on links in Lao Cai B. Net revenue fows on links in Binh Dinh

C. Net revenue fows on links in Thanh Hoa

Natural Hazard Exposure of Transport Networks The study assembled four main types of natural hazard datasets:

• Landslide susceptibility zonation maps include classification of the types, areas, and spatial distribution of existing and future landslides. These maps show the relative spatial likelihood for the occurrence of landslides by type and volume—or area (Fell et al 2008). • Flashflood susceptibility zonation maps similarly show the relative spatial likelihood that a heavy rain event will become stationary and protracted over an area (Hapuarachchi et al 2011). • Typhoon induced flood maps estimate the increases in storm surge heights induced by pressure gradients created by typhoons along coastal areas, which translate to over-land inundations (Lapidez et al 2015). • Fluvial flood maps show inundation caused by rivers overtopping their banks. Of the above hazards, only the fluvial flood map data offered a country wide spatial coverage, whereas all other types of hazard map data covered only certain parts of the country. The study assumed all hazard maps without any future climate change scenarios represent hazard events for the present risks in the year 2016. From the underlying datasets, the study selected spatial extents and areas where hazard levels exceeded the threshold needed to be considered catastrophic enough to cause transport failures.

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The study accessed some of the natural hazards maps with future climate change model outputs developed by MoNRE, based on the internationally recognized Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios (Meinshausen et al 2011). RCP4.5 scenarios assume that global emissions peak around the year 2040 and then decline, while RCP8.5 scenarios assume no decline in global emissions through the 21st century. In addition, the study looked at flashflood and landslide susceptibility maps for fifteen northern provinces generated for RCP4.5 and RCP8.5 scenarios in 2025 and 2050. The study also accessed fluvial flood maps for the whole country showing current flooding and future flooding under RCP4.5 and RCP8.5 scenarios in 2030, from the GLOFRIS (Winsemius et al 2013) model. See appendix B, including table B.2, for a detailed description of the hazard datasets.

Per the IPCC definition (IPCC 2014), the study conducted a hazard exposure analysis as the first component of a vulnerability assessment. Only areas of high and very high landslide and flashflood susceptibility as well as areas of flood depths greater than or equal to 1 meter were considered in the study’s extreme hazard scenarios. The analysis aimed to identify the types of hazards to which transport assets are most exposed to and the changes in the hazard exposures due to various climate change scenarios.

National-scale road and railways hazard exposures Table 2.6 shows the overall minimum and maximum total kilometers of the national-scale road and railway networks in Vietnam that are exposed to different extreme hazard levels. For every hazard where future climate change scenarios are considered, the lengths of road and railway networks exposed to extreme hazards would increase due to climate change. For instance, while approximately 720 km to 1,163 km of the national road network is exposed to extreme river flooding in the current flooding scenarios, the network lengths would increase to between 786 km and 1,180 km under a future RCP4.5 scenario. In the case of river flooding, the minimum exposure values correspond to the lowest return periods (two or five years), while the maximum exposure values correspond to the 1,000-year return period flood maps.4 While the road networks are most exposed to river flooding, railways are most exposed to landslides, primarily in the coastal provinces.

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Table 2.6. Lengths of National Roads and Railway Networks Exposed to Different Types of Hazards

National roads exposures National rail exposures Climate (kilometers) (kilometers) Hazard type Year scenario Min. Max. Min. Max.

— 2016 188 188 3 3

2025 197 197 3 3 RCP 4.5 Flashflood 2050 222 222 3 3 susceptibilitya

2025 211 211 3 3 RCP 8.5 2050 235 235 3 3

— 2016 720 1,163 88 117

River flooding RCP 4.5 2030 786 1,180 97 121

RCP 8.5 2030 785 1,174 97 119

— 2016 896 896 189 189

2025 276 276 7 7 RCP 4.5 Landslide 2050 320 320 9 9 susceptibilityb

2025 289 289 7 7 RCP 8.5 2050 334 334 10 10

Typhoon floodingc — 2016 783 783 123 123

Note: a. The flashflood susceptibility data cover only 15 northern provinces. b. The landslide susceptibility data for current scenarios cover 8 central and 15 northern provinces. The future climate scenarios cover only 15 northern provinces. c. The typhoon flooding data cover only 28 coastal provinces.

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By examining the spatial patterns of the hazard exposures presented in figure 2.7 and figure 2.8, additional insights can be derived by examining the spatial patterns of the hazard exposures for the national-scale road and railway networks respectively.5 Each of the figures shows (at the district- level) the percentage network kilometers exposed to the worst-cases of current (2016) levels. The spatial patterns of extreme hazard exposures reveal the districts with very high percentages (greater than 40 percent) of networks exposed to extreme hazards, especially for landslides, river flooding, and typhoon flooding. Based on the above findings, the study analysis indicates districts with elevated percentages of exposure face high potential for transport failures.

The study next considered the effects shown in climate change scenarios, examining the spatial changes to national-scale road and railway networks caused by exposures to extreme river flooding. Figure 2.9 shows the change in percentage kilometers with road links exposures to the 1,000-year return period river flooding. Panel A shows the effects in 2030 under the RCP 4.5 emission scenario and panel B illustrates the RCP 8.5 emission scenario. These changes compare to the figure 2.7b result. Though the worst-case scenarios of river flooding in general show a very slight decrease in risk, the results shared in figure 2.9 and 2.10 provide an important takeaway: climate change could cause some regions to face a substantial increase in network flooding. In consideration of advanced planning for climate change, such locations should be examined further for potential failure impacts.

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Figure 2.7. Hazard Exposure of National-Scale Roads Network in Vietnam

A. Extreme landslide susceptbility exposures B. Extreme 1,000-year river fooding exposures

C. Extreme typhoon fooding exposure D. Extreme fashfood susceptbility exposure

Note: Panel A= landslides, where landslide susceptibility is high and very high; panel B = river flooding, where flooding depth exceeds 1 meter for a 1,000-year event; panel C = typhoon flooding, where flooding depth exceeds 1 meter; and panel D = flashfloods, where flashflood susceptibility is high and very high.

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Figure 2.8. Hazard Exposure of National-Scale Railway Network in Vietnam

A. Extreme landslide B. Extreme 1,00-year susceptbility exposure river fooding exposure

C. Extreme typhoon fooding exposure D. Extreme fashfood susceptbility exposure

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Figure 2.9. Percentage Change in Exposure of National-Scale Road Network to 1,000-Year River Flooding by 2030

A. Percentage change B. Percentage change for fluvial flooding (RCP 4.5 2030) for fluvial flooding (RCP 8.5 2030)

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Figure 2.10. Percentage Change in Exposure of National-Scale Rail Network to 1,000-Year River Flooding by 2030

A. Percentage change B. Percentage change for fluvial flooding (RCP 4.5 2030) for fluvial flooding (RCP 8.5 2030)

Province-scale hazard exposure of roads The study conducted similar analysis for the province-scale roads. Table 2.7 shows the minimum and maximum kilometers of the province-scale road networks exposed to various extreme hazard levels, as specified by the underlying hazard data. The results show that Lao Cai and Binh Dinh experience the highest exposures to extreme landslides, while in Thanh Hoa, river flooding causes the highest exposures. Generally, the current and future climate change scenarios indicate minor differences between the worst-case hazard exposures.

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Table 2.7. Exposure of Province Roads Networks to Hazards

Road hazard exposures (km.) Climate Hazard type Year Lao Cai Binh Dinh Thanh Hoa scenario Min. Max. Min. Max. Min. Max.

— 2016 88 88

2025 88 88 Flashflood RCP 4.5 2050 93 93 susceptibility 2025 90 90 RCP 8.5 2050 132 132

— 2016 57 57 534 539 1,803 1,841

River flooding RCP 4.5 2030 57 57 535 543 1,875 1,910

RCP 8.5 2030 55 57 534 539 1,864 1,889

— 2016 142 142 801 801 921 921

2025 145 145 Landslide RCP 4.5 2050 180 180 susceptibility 2025 163 163 RCP 8.5 2050 210 210

Typhoon flooding — 2016 315 315 592 592

Geospatial analyses highlight the locations where much higher percentages of road network kilometers are exposed to extreme hazards. Figure 2.11 shows the commune-level percentages of roads exposed to hazards in Lao Cai, where the spatial landslides are more prevalent more communes are prone to flashfloods and river flooding. As shown in figure 2.13, exposures of roads to landslides are far more pronounced throughout Binh Dinh and similarly in Thanh Hoa, as shown in figure 2.15. From these maps, the study identified the communes facing increased multiple hazard threats due to the high percentages of roads exposed one or more hazards (see appendix D). The study also looked at climate change-induced exposure scenarios for hazards affecting provincial road networks. In Lao Cai, figure 2.12 shows changes in exposures to extreme landslides in 2050 under RCP 4.5 and RCP 8.5 scenarios, which results in a greater number of communes experiencing increased percentages of roads exposed to extreme landslides, with a more pronounced increase under the RCP 8.5 climate scenario. In figure 2.14, similar comparisons are seen in Binh Dinh for extreme 1,000-year river flooding under climate scenarios, with an increased exposure of roads to extreme river flooding. The results for Thanh Hoa, shown in figure 2.16, highlight substantial clusters of communes where climate change causes a potentially significant (greater than 40 percent) increase of roads exposed to extreme 1,000-year river flooding (see appendix D).

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Figure 2.11. Hazard Exposure of Province Roads Network in Lao Cai

A. Extreme landslide susceptbility exposures B. Extreme fashfood susceptbility exposures

C. Extreme river fooding

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Figure 2.12. Percentage Change in Exposure of Lao Cai Road Network Links to Landslides by 2050

A. RCP 4.5 2050 B. RCP 8.5 2050

Figure 2.13. Hazard Exposure of Province Roads Network in Binh Dinh

A. Extreme landslide B. Extreme river flooding C. Extreme typhoon flooding susceptibility exposures exposures exposures

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Figure 2.14. Percentage Change in Exposure of Binh Dinh Road Network Links to 1,000-year River Flooding by 2030

A. RCP 4.5 2030 B. RCP 8.5 2030

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Figure 2.15. Hazard Exposure of Province Roads Network in Thanh Hoa

A. Extreme landslide susceptibility exposures B. Extreme flashflood susceptibility exposures

C. Extreme typhoon flooding exposures

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Figure 2.16. Percentage Change in Exposure of Thanh Hoa Road Network Links to 1,000-Year River Flooding by 2030

A. RCP 4.5 2030 B. RCP 8.5 2030

Notes

1. Data accessed at: http://cangben.viwa.gov.vn.

2. GSO transport statistics, available at: https://www.gso.gov.vn/default_en.aspx?tabid=781.

3. GSO transport statistics, available at: https://www.gso.gov.vn/default_en.aspx?tabid=778.

4. No variation between the minimum and maximum values results from the underlying hazard data having one realization.

5. The maps in figure 3.9 and figure 3.10 provide a sense of the spatial extents and disaggregation of the underlying hazard data, where flashflood exposures are clearly confined to northern areas because the underlying hazard data only existed for those regions. Similarly, landslides exposures are limited by the spatial coverages of the underlying data. As stated previously, only river flooding datasets offered whole-nation coverage.

References

IPCC (Intergovernmental Panel on Climate Change). 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by V. R. Barros, C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White. London and New York: Cambridge University Press. https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-PartB_FINAL.pdf.

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Fell, Robert, Jordi Corominas, Christophe Bonnard, Leonardo Cascini, Eric Leroi, and William Z. Savage. 2008. “Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning.” Engineering Geology 102 (3): 85–98. doi: 10.1016/j.enggeo.2008.03.014.

Hapuarachchi, H. A. P., Q. J. Wang, and T. C. Pagano. 2011. “A Review of Advances in Flash Flood Forecasting.” Hydrological Processes 25 (18): 2771–84. doi: 10.1002/hyp.8040.

JICA (Japan International Cooperation Agency), MoT (Ministry of Transport, Socialist Republic of Vietnam), and TDSI (Transport Development and Strategy Institute, Vietnam). 2000. The Study on the National Transport Development Strategy In the Socialist Republic Of Vietnam (VITRANSS)— Technical Report No. 3: Transport Cost And Pricing In Vietnam. Tokyo, Japan: JICA, ALMEC Corporation, and Pacific Consultants International. http://open_jicareport.jica.go.jp/pdf/11596814.pdf.

Koo, J., Z. Guo, U. Wood-Sichra, and Y. Ru. 2018 (unpublished). Spatial Disaggregation of 2015 Sub- National Crop Production Statistics in Vietnam. Internal data provided by IFPRI for the study. Washington, DC: International Food Policy Research Institute.

Lapidez, J. P., J. Tablazon, L. Dasallas, L. A. Gonzalo, K. M. Cabacaba, M. M. A. Ramos, J. K. Suarez, J. Santiago, A. M. F. Lagmay, and V. Malano. 2015. “Identification of Storm Surge Vulnerable Areas in the Philippines through the Simulation of Typhoon Haiyan-induced Storm Surge Levels over Historical Storm Tracks. Nat Hazards Earth Syst Sci 3 (2): 1473–81. doi: 10.5194/nhess-15-1473- 2015.

Meinshausen, M., S. J. Smith, K. Calvin, J. S. Daniel, M. L. T. Kainuma, J-F. Lamarque, K. Matsumoto, S. A. Montzka, S. C. B. Raper, K. Riahi, A. Thomson, G. J. M. Velders, D.P. P. van Vuuren. 2011. “The RCP Greenhouse Gas Concentrations and their Extensions from 1765 to 2300. Climatic Change 109 (1-2): 213–41. doi: 10.1007/s10584-011-0156-z.

JICA (Japan International Cooperation Agency) and MoT (Ministry of Transport, Vietnam). 2010. The Comprehensive Study on the Sustainable Development of Transport System in Vietnam (VITRANSS II). Tokyo: Japan International Cooperation Agency (JICA) and ALMEC Corporation. http://open_jicareport.jica.go.jp/700/700/700_123_11999943.html.

MoT (Ministry of Transport, Vietnam). 2015. Seaport Classification. Hanoi: VINAMARINE (Vietnam Maritime Administration), Ministry of Transport, Vietnam. http://www.vinamarine.gov.vn/Index.aspx?page=reportshipdetail&id=13.

Systra. 2018 (unpublished). Strengthening of the Railway Sector in Vietnam. Report 3.2: Railway Demand Analysis—Freight. Report provided by Systra for the study.

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Chapter 3: Vulnerability, Criticality, and Risk Assessment

This chapter brings the results of the analyses presented in Chapter 2, to analyze vulnerability, criticality and risk assessment of flow disruptions on the assembled transport networks for Vietnam. Here, the analysis considers an exhaustive set of exposure scenarios for failure, assessing the socio- economic impacts through a variety of metrics to identify the network locations with the greatest impacts. For each network, the analysis quantifies the ranges of disruptive impacts across each hazard to understand whether hazard impacts will increase in future climate scenarios. Vulnerability and Criticality Assessment After identifying the network links exposed to different extreme levels of hazards, a vulnerability assessment can be conducted. Vulnerability assessments, completed within the context of the hazards, result in understanding the relative impacts of hazards on the continued transport availability. Once the vulnerability assessment is done, a criticality assessment can provide results in ranking network elements, based on their relative impacts on the serviceability of the transport networks (Arga Jafino 2017). The criticality analysis performs an exhaustive analysis of the individual network links’ “what if failure” scenarios. Having been exposed to the extreme levels of hazards, these links exist in potential failure locations. The criticality analysis serves as useful as a first step in exploring and evaluating the systemic performance in a generalized context, impartial to the nature of the external shock event. The assessment can identify the most critical links in the networks, which can then be used to help select a smaller set of links for further hazard risk analysis. Such exploratory analysis can be used to examine whether the most critical links identified have also been exposed to hazards, thus providing a strong case for detailed site-specific analysis. The criticality assessment aims to answer the following questions:

• Which links and routes along the networks are most important to the continued service of network flows? • Which individual links failures result in macroeconomic disruptive impacts, and what are the magnitudes of those disruptive impacts? • Which individual links failures have most impacts on the costs of rerouting flows? In response, the study created and estimated the following criticality metrics:

• Average annual daily freight (AADF) disruptions: The potential daily tonnage of freight flow disrupted by the failure of individual network links exposed to any hazard considered in the study. • Total macroeconomic loss: The sum of the direct and indirect macroeconomic losses in US$ per day, due to the losses of commodity flows that create economic supply and demand imbalances in the economy. Such losses arise from individual links whose failure causes trip isolations, where the only available route option along the origin-destination (OD) route becomes physically inaccessible through the failed edge.

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• Freight redistribution cost metric: The total difference between the post-disruption and pre-disruption generalized cost estimates of all OD flows rerouted due to edge failure. The freight redistribution costs are assigned to the edge whose failure causes those redistributions. These values show the most cost-effective links in the network. • Total economic impact metric: The overall economic criticality of network links is measured as the sum of their macroeconomic losses and the freight redistribution costs incurred due to failures. For the vulnerability and criticality assessments, the analysis selected individual network links based on the spatial intersection of networks and the hazards listed in table 3.1. The selection was limited to the landslide and flashflood susceptibility areas with high and very high likelihood values as well as river and typhoon flooding areas with flood depths ≥1 meter. Each selected transport network’s links was intersected with all selected hazards to identify the exhaustive set of all unique nodes and links subjected to a hazard. The subsequent sections present and discuss only the following network results:

Table 3.1. Selected Numbers of Single Link Failure Scenarios for Different Transport Networks

Transport network selected Unique single link failure scenarios

National-scale roads 968

National-scale railways 164

Lao Cai province roads 1,299

Binh Dinh province roads 11,042

Thanh Hoa province roads 26,852

National-Scale Road Network Criticality Figure 3.1 shows the national-scale road network criticality results, including the maximum AADF disruption values in figure 3.1A. The result highlights the network locations with very high flows, where there are threats of systemic disruptions due to exposures to extreme hazards. The results show a cluster of roads around Ho Chi Minh and large sections of the QL1A trans-Vietnam highway with high AADF flows that could be disrupted by hazards. The highest ranges of potential AADF disruptions substantial, between 124,000 to 156,000 tons per day. Figure 3.1B shows the maximum total macroeconomic losses in US$ million per day, based on the identified cases, where some of the AADF disruptions reported in figure 3.1A result in immediate macroeconomic losses. These trip isolations, where the commodities comprising the AADF mix

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cannot be transported, create supply and demand imbalances in the macroeconomic system. The process of estimating these macroeconomic losses is explained in appendix A. This process can be treated as a worst-case assumption; realistically a disruption lasting a day will probably not produce macroeconomic losses. Therefore, the results can be interpreted to highlight the macroeconomic importance of network links. Quite significantly, the results show that disruption of the major link toward Ho Chi Minh (DT743) could potentially create an estimated US$0.074 to US$0.44 million per day in macroeconomic losses. If this link is disrupted for a significant time, such losses could realistically materialize. The macroeconomic loss results also, importantly, highlight that large disruptions in tonnage might not necessarily result in significant economic losses in every case. In some cases, economic gains have resulted from regions substituting for production capacity losses in other regions, leading to reduced macroeconomic impacts and even net macroeconomic gains post- disruption. Where the disrupted AADF flows can be rerouted along alternative routes, freight redistribution cost increases along disrupted links create significant failure impacts on the national road network. However, this comes at the price of increased transportation costs (figure 3.1c), where the values show the systemic freight redistribution costs on the whole network reported at the individual failed links. The highest rerouting costs can range between US$0.67 million per day (minimum flow disruption scenario) to US$1.90 million per day (maximum flow disruption scenario). Most of these are throughout sections of the QL1A trans-Vietnam highway, a very significant route in the country. In particular, the sections of this highway through Nghe Ah to Thanh Hoa show highest impacts on rerouting costs. The economic impacts of road disruptions are shown in figure 3.1D, which combines the results from figures 3.1B and 3.1C. As discussed, the significant economic impacts are created by redistribution cost increases, rather than macroeconomic losses from trip isolations—with a few exceptions, such as the DT743 link. Based on the model results, it could be worth considering whether the high costs of rerouting will result in companies’ non-use of the roads while waiting for disrupted roads to be fixed, or partial use of the rerouting options during route reconstruction. This behavior has not been explored here, but this analysis provides a means to consider such possibilities.

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Figure 3.1. National-Scale Roads Criticality Results

A. Maximum daily tons disrupted B. Maximum macroeconomic losses

C. Maximum rerouting loss D. Maximum total economic loss

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National-Scale Railway Criticality Figure 3.2 shows the results for the railway network. Given the single route structure along most of the railway network, link failures impact significant routes with very high flows, resulting in worst- case AADF disruptions between 8,000 to 10,000 tons per day (figure 3.2A). Following the cases of AADF disruptions, figure 3.2B shows maximum total macroeconomic losses in US$ million per day, based on the assumption that the AADF losses reported result in immediate economic impacts. Quite significantly, the results show that railway disruptions can potentially result in very high economic losses, with economic losses on the busiest routes ranging from US$2.3 to 2.6 million per day. These results show that though the overall railway network usage is much less compared to roads, use is significant in the regions vulnerable to disruptions. The high impacts caused by railway distributions could be responsible for declining railway usage in Vietnam

Figure 3.2C highlights the freight redistribution costs, with a few links causing only minor impacts on redistribution cost increases along the railway network; the highest rerouting costs, at most US$9,000 per day, occur around the Hanoi hub.

The economic impacts of railway disruption (figure 3.2D) are created by the macroeconomic losses resulting from trip isolation redistribution cost increases, rather than redistribution cost increases alone. Based on the model results, the railway network shows very little redundancy in coping with disruptions.

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Figure 3.2. National-Scale Railway Criticality Results

A. Maximum daily tons disrupted B. Maximum macroeconomic losses

C. Maximum rerouting loss D. Maximum total economic loss

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Province-Scale Road Criticality At the province scale, the study estimates road network criticalities in terms of:

• Net revenue flow disruptions, which potentially arise from hazard induced disruptions along accessible routes toward the commune centers. • Economic impacts estimates, which represent the sum of the economic losses (the net revenue disrupted) due to the lack of accessible routes toward the commune centers, and the increases in rerouting costs, where post-disruption access to commune centers is maintained.

The results that follow highlight, through the net revenue flow disruption maps, potential disruptions to the network links most important in providing access to commune centers. Also, the maps highlight any rerouting options around each disrupted network link. Taking such rerouting into account, the results underline the economic impacts of disruptions. Crucially, the results show that the existence of rerouting options decreases the impacts of flow disruptions. The economic loss results prominently reveal the network links used most heavily for net revenue generation, whose failures would result in complete lack of access. To give a sense of the worst-case scenarios of failures, the sub-sections that follow show only the maximum net revenue flow disruptions and maximum economic impacts.

Lao Cai province roads disruptions The heat-map for Lao Cai province in figure 3.3A shows the maximum net revenue flow disruptions due to hazard-driven road failures. The spare concentration of roads in Lao—especially in the Bao Yen, Bat Xat, and Van Ban districts—focuses flow disruptions along fewer routes. Potentially, these net revenue flow disruptions can cause up to US$27,000 per day in economic losses. The economic impacts shown in figure 3.3B reflect how the existence of rerouting options absorbs most of the potential disruptions in net revenue flows. However, some link failures cause complete loss of access to commune centers, resulting in worst-case economic losses of up to US$26,000 per day.

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Figure 3.3. Maximum Estimated Disruption in Net Revenue and Maximum Economic Loss for Lao Cai Province Road Network

A. Maximum net revenue disruption B. Maximum economic loss in Lao Cai province in Lao Cai province

Binh Dinh province roads disruptions In figure 3.4, heat-maps for Binh Dinh province show the maximum net revenue flow disruptions (panel A) and maximum economic impacts (panel B) caused by hazard-driven road failures. Most of the network is very redundant, leading to insignificant impacts due to disruptions. However, a significant cluster in the Quy Nhon district, which has very high disruptive impacts, could see potential economic impacts of US$56,000 per day.

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Figure 3.4. Maximum Estimated Disruption in Net Revenue and Maximum Economic Loss for the Binh Dinh Province Road Network

A. Maximum net revenue disruption B. Maximum economic loss in Binh Dinh province in Binh Dinh province

Thanh Hoa province roads disruptions In figure 3.5, heat-maps for Thanh Hoa province show the maximum net revenue flow disruptions (panel A) and maximum economic impacts (panel B) resulting from hazard-driven road failures. Here again, most of the network is very redundant, leading to insignificant disruption-induced impacts. However, significant clusters in the Bim Son district with the highest disruptive impacts could see potential economic impacts of US$67,000 per day.

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Figure 3.5. Maximum Estimated Disruption in Net Revenue and Maximum Economic Loss for the Thanh Hoa Province Road Network

A. Maximum net revenue disruption B. Maximum economic loss in Thanh Hoa province in Thanh Hoa Province

Risk Assessment Following the assessment of network link criticalities, the risk of extreme hazard failures can be estimated by combining the knowledge of the hazards and impacts into one metric. The main aim of the risk estimation is to identify:

• Which types of hazards have the most impacts on transport links? • What are the changes in the hazard impacts due to different climate change scenarios? The losses here signify the daily economic impacts, estimated in the criticality assessment, and multiplied by certain duration of disruption—during which the affected network link is assumed to be out of operation. To differentiate between the severities of events with different probabilities, the analysis assumed that the duration of disruption will depend upon what percentage of the network link’s length is exposed to the extreme hazard for that event. Limitation: The risk calculation results presented in the subsequent sections show very high values for the landslide and typhoon flooding events—and to some extent the flashflood events—based on the assumption that these hazards have a probability equal to one. The analysis made the assumption in the absence of any probabilistic information for these hazards. Because only the underlying river flooding hazards information is probabilistic, only the results for the river flooding risks capture the true nature of risk estimation.

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National-scale networks The results that follow for the national-scale road and railway network assume a maximum duration of disruption of 10 days, with a 500 m threshold length of exposure of a network link. Hence, if a network link length greater than 500 m was exposed to the chosen extreme hazard scenario, the results assumed a 10-day disruption. Below that length, the disruption duration depended upon the fraction of network length exposed to the hazard.

National-scale road network risks Figure 3.6 shows the maximum value of economic risks on the national-scale road network due to current 2016 hazards of, respectively, (A) landslide susceptibility, (B) river flooding, (C) typhoon flooding, and (D) flashflood susceptibility. The results aim to highlight the hazard-specific high-risk locations on the network, which can be prioritized for further detailed investigations into climate resilience. The analysis shows that risks along key sections of the QL1A trans-Vietnam highway are mainly driven by landslides and typhoon flooding, while river flooding affects links around Ho Chi Minh and Thua Then Hue, and flashfloods affect some mountain provinces due to the underlying hazards concentrated only in those regions.

To understand the changes in the hazard impacts due to climate change scenarios, the analysis compares road network risks due to future climate hazards with those due to the current day hazards. Such a comparison is possible only for river flooding because it is the only hazard that gives full national coverage of different probabilistic hazards under climate change scenarios. Both climate change scenarios for river flooding see a substantial increase in the value of systemic risks of future failure of national-scale road network links (figure 3.7). On almost all affected network links, the potential risks of failure due to river flooding increase by at least 40 percent in the future 2030 hazard scenarios, a substantial increase in risks that highlight a strong case to invest in building networks resilient to future climate hazards.

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Figure 3.6. Maximum Estimated Risks for National-Scale Road Network Links

A. Landslide susceptibility maximum risks B. River flooding maximum risks

C. Typhoon flooding maximum risks D. Flashflood susceptibility maximum risks

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Figure 3.7. Percentage Change in Maximum Failure Risks of National-Scale Road Network Links for Future 2030 River Flooding under Climate Scenarios

A. RCP 4.5 2030 B. RCP 8.5 2030

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National-scale railway network risks

Similar to the results of the previous section, figure 3.8 shows the maximum value of economic risks on the national-scale rail network links due to current 2016 hazards of, respectively: (A) landslide susceptibility, (B) river flooding, (C) typhoon flooding, and (D) flashflood susceptibility. The analysis shows that large sections of the railways are at risk mainly due to landslides and typhoon flooding, while river flooding affects a small section of links around Quang Nam and Thua Then Hue, and flashfloods affect some mountain provinces due to the underlying hazards concentrated only in those regions. For the railway network as well, the risks due to future climate change scenario-driven river flooding hazards in 2030 compare with those due to the current day river flooding hazards in 2016 (figure 3.9). Under both climate change scenarios of river flooding, systemic risks of national-scale rail network failure increases substantially in the future. Similar to the national-scale road network links, at almost all affected network links the potential risks of failure due to river flooding face a substantial increase of at least 40 percent in the future 2030 hazard scenarios, highlighting a strong case in Vietnam to invest in building national railways resilient to future climate hazards.

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Figure 3.8. Maximum Estimated Risks for National-Scale Railway Network

A. Landslide susceptibility maximum risks B. River flooding maximum risks

C. Typhoon flooding maximum risks D. Flashfloods susceptibility maximum risks

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Figure 3.9. Percentage Change in Maximum Failure Risks of National-Scale Rail Network for Future 2030 River Flooding under Climate Scenarios

A. RCP 4.5 2030 B. RCP 8.5 2030

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Province-scale analysis

The results for the province-scale road networks assume a maximum disruption duration of 10 days, with a 100 m exposure threshold length of a network link. Hence, if a network link greater than 100 m was exposed to the chosen extreme hazard scenario, the results assumed a 10-day disruption. Below that length, the duration of disruption depended upon the fraction of the length exposed to the hazard.

Lao Cai province-scale roads risks

The analysis highlights key sections of the network links in the Van Ban and Sa Pa districts affected by landslides, flashfloods, and river flooding (figure 3.10), whose failures results in high risks due to lack of access to their nearest commune centers.

Figure 3.10. Maximum Risks of Lao Cai Province-Scale Road Network

A. Landslide susceptibility maximum risks B. Flashflood susceptibility maximum risks

C. River flooding maximum risks

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The analysis shows that along several roads in the Bat Xat, Lao Cai, Van Ban, and Bao Yen districts the future landslide-driven failure risks of large continuous sections of the road network increase substantially by at least 40 percent, with more severe changes under RCP 8.5 scenarios (figure 3.11). Hence, strongly indicating that future access to economic opportunities in this part of the province will be severely affected without investment in building climate-resilient roads.

Figure 3.11. Percentage Change in Maximum Failure Risks of Lao Cai Province-Scale Road Network Links for Future 2050 Landslide Susceptibility under Climate Scenarios

A. RCP 4.5 2050 B. RCP 8.5 2050

Binh Dinh province-scale roads risks

The analysis highlights key sections of the road network links in the Tuy Phuoc and Quy Nhon districts affected by landslides, typhoon flooding, and river flooding (figure 3.12), whose failures result in high risks due to large areas of economic activity experiencing a lack of access to their nearest commune centers. The changing percentages of maximum risks due to climate change in the Binh Dinh province clearly indicate increasing risks due to road link failures from future climate change driven hazard scenarios, with expected annual losses in almost every case substantially increased by at least 40 percent (figure 3.13). Again, there is a strong indication that in the future, access to economic opportunities will be severely affected without investment in building climate-resilient roads.

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Figure 3.12. Maximum Risks of Binh Dinh Province-Scale Road Network

A. Typhoon flooding maximum risks B. Landslide susceptibility maximum risks

C. River flooding maximum risks

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Figure 3.13. Percentage Change in Failure Risks of Binh Dinh Province-Scale Road Network for Future 2030 River Flooding under Climate Scenarios

A. RCP 4.5 2030 B. RCP 8.5 2030

Thanh Hoa province-scale roads risks

The analysis highlights key sections of network links in Bim Son district affected by landslides, whose failures results in high risks due to large areas of economic activity experiencing a lack of access to their nearest commune centers (figure 3.14). As shown in figure 3.15, the changing maximum risks due to climate change in the Thanh Hoa is a clear indication of increasing risks due to road link failures from future climate change-driven hazard scenarios, with expected annual losses in almost every case substantially increased by at least 40 percent. Again, there is a strong indication that access to economic opportunities may be severely affected without investment in climate resilience.

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Figure 3.14. Maximum Estimated Risks for Thanh Hoa Province-Scale Road Network

A. Typhoon flooding maximum risks B. Landslide susceptibility maximum risks

C. River flooding maximum risks

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Figure 3.15. Percentage Change in Failure Risks of Thanh Hoa Province-Scale Road Network for Future 2030 River Flooding under Climate Scenarios

A. RCP 4.5 2030 B. RCP 8.5 2030

Uncertainties in Criticalities and Risks Uncertainties are a natural part of estimating the criticalities and risks of failures of transport links. The estimated network link criticality uncertainties are sensitive to the modeled ranges of flow assignments and rerouting on the networks as well as to the parameters in the macroeconomic loss model. The result shown in figure 3.16A highlights the high variability in the estimated road losses, where the largest losses vary between US$0.67 and US$1.9 million per day. These losses, mainly driven by the increases in transport costs from rerouting of network flows, follow individual link failures. The resulting increased transport costs are primarily influenced by the volumes of rerouted freight. Hence, the uncertainties in estimated losses are mainly sensitive to the volumes of disrupted flows. As discussed in the section “Rail Failure Analysis Results” in Chapter 5, the economic losses for railways are mainly driven by macroeconomic losses. The macroeconomic loss estimates take into account the ability of the multiregional economy to substitute for lost freight in a region, which in turn depends on the percentage of regional economic production dependent upon the lost freight. Substitutions for a high failure percentage will not be sufficient to offset macroeconomic losses due to freight disruptions; however, a low failure percentage allows a multiregional economy to substitute and offset macroeconomic losses. The large fluctuations in economic losses shown in figure 3.16B are mainly sensitive to these substitution effects in the macroeconomic model. Hence, losses for several links range between very small values (~0)—when the volumes of disrupted tonnages do not affect the regional economies—and very high values (in excess of US$2 million per day), because the volumes of disrupted tonnages substantially disrupt the regional economies’ ability to substitute.

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Figure 3.16. Estimated Ranges of Daily Economic Losses for Individual Network Link Failures in Vietnam’s National-Scale Road and Rail Networks

A. Roads: B. Railways: Range of economic losses due to link failures Range of economic losses due to link failures

The estimated risks are sensitive to the type of hazards, in addition to factors influencing the criticalities. Thus, comparing ranges of hazard-specific risks across different scenarios of the same hazard type shows how the hazard uncertainties influence network risks. Such a comparison is made for the case of national-scale road and railway network risks due to river flooding in current and future conditions under the RCP4.5 and RCP8.5 scenarios. Figures 3.17A and 3.17B show the ranges of current (2016) and future (2030) climate change-driven river flooding risks, calculated as the Expected Annual Economic Losses (EAEL) in US$ millions for 10- day maximum disruption of individual network links, in the national-scale road and railway network respectively. The results show the uncertainties of failure risks due to climate change significantly increase under the RCP4.5 and RCP8.5 scenarios. Also, both climate scenarios show significant increases in the maximum estimates of risks, due to increasing severity and frequency of future extreme river flooding. Analysis for the road network shows the highest EAEL increases from US$0.44 to US$0.88 million, an increase of 100 percent. For the railway network, the highest EAEL increases from US$1.4 to US$2.8 million, a significant increase of 100 percent in highest losses. Figures 3.18 to 3.20 present the uncertainties in estimating criticalities and risks for the three province-scale networks (Lao Cai, Binh Dinh, and Thanh Hoa). The plot on the left panel (panel A) of each figure shows the range of daily economic impacts for all impacted links, ranked by maximum daily impacts. The plot on the right panel (panel B) shows the EAEL for all links affected by current and future extreme river flooding. Each criticality result indicates the dominance of a relatively small percentile of links in each province. High losses in Lao Cai are concentrated in approximately the top 20 percent of links, in Binh Dinh they are concentrated in approximately the top 10 percent of links, and in Thanh Hoa in approximately the top 5 percent of links. The high ranges of losses occur mainly for those links whose failures result in complete lack of access to commune centers. The magnitudes of such losses are mainly sensitive to the changing net revenues assigned to these links, which in turn are sensitive to the changing tonnages of crop (rice) productions assumed in the flow estimation model. The analysis in figure 3.18 shows that for Lao Cai, the highest daily economic impacts range from US$23,000 to US$26,000 per day, for Binh Dinh (figure 3.19) the highest daily losses range from US$47,000 to US$56,000 per day, and for Thanh Hoa (figure 3.20), the highest daily losses range from US$64,000 to US$67,000 per day.

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Figure 3.17. Estimated Ranges of Minimum and Maximum Risks for Individual Link Failures Due to Current and Future River Flooding on National-Scale Networks

A. Roads: River flooding risks

B. Railways: River flooding risks

However, these ranges of highest losses might not necessarily be recorded for the same links, as the underlying distributions of net revenue allocated to links vary widely. The risk results, in the context of river flooding, show significantly increased uncertainties of failure risks due to climate change, under both RCP4.5 and RCP8.5 scenarios. Similar to the national-scale analysis, both climate scenarios show significant increases in the maximum risk estimates, due to increasing severity and frequency of extreme river flooding in the future. In both future climate scenarios for Lao Cai road network (figure 3.18), the link with the highest maximum EAEL (around US$20,000) showed a maximum EAEL of around US$4,000 in the current scenario—reflecting an increase of more than 400 percent. Analysis for the Binh Dinh road network (figure 3.19) shows the link with the highest maximum EAEL of around US$4,500 in the future RCP4.5 climate scenario had a maximum EAEL in the current scenario, approximately US$450, indicating an increase of more than 900 percent. In the Thanh Hoa road network, analysis shows the link with highest maximum EAEL (approximately US$11,900) in the future RCP 8.5 climate scenario had a maximum EAEL of about US$300 in the current scenario, an increase of more than 3,800 percent. All three provinces show substantial increases in risks due to climate change-driven flooding for individual links.

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Figure 3.18. Estimated Ranges of Daily Economic Losses and Risks Due to Current and Future River Flooding of Individual Link Failures on the Lao Cai Road Network

A. Daily economic losses in Lao Cai

B. River flooding risks in Lao Cai

Figure 3.19. Estimated Ranges of Daily Economic Losses and Risks Due to Current and Future River Flooding of Individual Link Failures on the Binh Dinh Road Network

A. Daily economic losses in Binh Dinh

B. River flooding risks in Binh Dinh

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Figure 3.20. Estimated Ranges of Daily Economic Losses and Risks Due to Current and Future River Flooding of Individual Link Failures on the Thanh Hoa Road Network

A. Daily economic losses in Thanh Hoa

B. River flooding risks in Thanh Hoa

Criticalities and Vulnerabilities of Ports The following section presents criticality and vulnerability assessment results for the air transport, inland waterways, and maritime sectors at the major ports scales, as these are the most important assets on these networks. The results aim to provide evidence on:

• The main ports in three sectors identified with known exposures to extreme hazards. • The effect of climate change on the changing risks of exposure. The results demonstrate that, for each sector, the risks of climate change-driven hazard exposures will increase.1 For example, the maximum hazard probability of flooding of the Ho Chi Minh port increases to 0.2 (1 in 5-year flooding) under the RCP4.5 and RCP8.5 scenarios, in comparison to the 0.04 (1 in 25-year flooding) probability. From this, the analysis concludes the Ho Chi Minh port will be about 5 times more prone to frequent flooding in the future, and observed similar trends for all major ports in every sector.

Based on risk estimates for airports produced with older statistics, the analysis reveals that only Da Nang airport shows significant AADF flows exposed to extreme hazard. Since 2006 development around Da Nang has increased, and at present Da Nang serves as a major passenger hub in central Vietnam.2 Because of this, impacts on the airline sector should be interpreted in terms of passenger usage, a much more substantial metric than cargo transport.

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Results show exposure to extreme hazards in the major maritime ports of Ho Chi Minh, Hai Phong, and Can Tho, with analysis for the Ho Chi Minh and Can Tho ports clearly indicating that climate change will increase the frequency of extreme hazards. The significance of these ports to the economy of Vietnam is immense, with the analysis showing that under worst-case scenarios approximately 68,000 to 106,000 tons of cargo flows per day could be affected. Even though this study included domestic flows, these ports have far greater significance as major import and export hubs for Vietnam.

In terms of the country’s inland waterway ports (a total of around 41), results suggest that major inland hubs in An Giang, Hai Phong, Thai Binh, Quang Ninh, and Ho Chi Minh are identified as exposed to extreme hazards, with the frequency of hazard exposures for An Giang increasing in the future due to climate change. The implications of disruptions to these ports could be immense, with analysis indicating that under worst-case scenarios approximately 25,000 to 55,000 tons of cargo flows per day could be affected.

Notes 1. Appendix C shows detailed results of vulnerability assessment of the airports, major identified maritime ports, and major identified inland waterway ports. The analysis showed the 44 inland waterway ports and 10 maritime ports with significant commodity flows were exposed to extreme hazards, mostly typhoon flooding and river flooding.

2. Source (in Vietnamese): https://web.archive.org/web/20130729200548/http://www.mt.gov.vn/PrintView.aspx?ArticleID=106 63.

Reference Arga Jafino, Bramka. 2017. Measuring Freight Transport Network Criticality: A Case Study in Bangladesh. Delft, Netherlands: TU Delft (Delft University of Technology). http://resolver.tudelft.nl/uuid:0905337b-cdf7-4f6e-9cf6-ac26e4252580.

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Chapter 4: Adaptation Strategy and Analysis

This chapter presents the adaptation options considered for national-scale and provinces-scale road network assets, evaluating each asset the ranges of adaptation options and the associated costs and benefits. Following this, the chapter discusses results of a detailed cost-benefit analysis for each network asset under different hazard scenarios. Scope and Purpose of Adaptation Analysis Adaptation planning is a much larger process than discussed in this study. Ideally, adaptation options should involve a wide range of measures including, among others, structural, institutional, and social changes. Also, the types of adaptation options depend upon the context of the specific country and the financing objectives, which govern the types of objectives outlined by different multidonor- funded development agencies (Ebinger and Vandycke 2015). This study recommends several proposed guidelines on the steps required for detailed site-specific adaptation options in Vietnam, neighboring Lao PDR, and other countries, based on an understanding of local conditions (ICEM 2017a and 2017b; MoPWT Laos 2008; CSIR et al 2011). The reader can refer to these studies to understand the step-by-step guidelines in the adaptation planning and implementation process. Adaptation options explored in this study look specifically at measures intended to improve the structure reliability of road assets to make them more resilient to climate change impacts. These can also be called climate resilience strategies. The focus here is specifically on evaluating the benefits of structural engineering options to make the road assets structurally “climate proof.” Therefore, the approach adopted here consists of exploring engineering options of adaptation targeted to improve or create climate-resilient engineering standards of design for various aspects of road assets— subsurface conditions, upgrading material specifications, cross section and standard dimensions, drainage and erosion, and protective engineering structures (ADB 2011). The levels of hazard exposures of road assets selected here are the most extreme based on their identified magnitudes: selected landslides and flashfloods susceptibilities are high and very high; selected river and typhoon flooding are greater than 1 meter. Hence, the study assumes these levels of hazards will potentially cause catastrophic failures to road assets, i.e., physically, the road assets will be damaged and lose their ability to provide service and will require rehabilitation. This assumption implies that the purpose of the adaptation option is to make road assets resilient to catastrophic failures in order to avoid rehabilitation and thus maintain the continuity of service flows. The generalized evaluation of adaptation options presented here creates a high-level indicative assessment of transport systems and their climate resilience strategies; evaluation of adaptation options is a very site-specific problem, and depends on the detailed investigation of, among others, local terrains, structural dimensions of assets, conditions of assets, and specific failure mechanisms due to hazards. Estimating adaptation costs is a very site-specific problem and in particular different types of engineering-based adaptation options require detailed site investigation and structural design calculations of road asset design standards. At best, this analysis presents a first-order screening whereby the efficacy of different types of adaptation options can be compared and locations and assets with high (or low) adaptation benefits can be narrowed down for further investigation.

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As discussed later, in Vietnam the vulnerability to slope stability of roads due to landslides and flashfloods is a major issue. Unfortunately, the underlying data in this study does not provide any information on road slopes inclines and areas, which would have been used to fully quantify adaptation options to build climate resilience to landslides and flashfloods.

Identifying Failures by Climate Change Driven Hazard Threats Listed below are some of the types of failures of road assets due to hazards—landslides and flooding (river, flash, and typhoon)—in Vietnam. This is not an exhaustive list, but rather a list of main causes of failures that affect the continuity of road usage. In several instances two or more of these failure types could manifest simultaneously: • Erosion of the road pavement; • Embankment failures: slope erosion; • Cut slope failure; • Drainage failures: a) pavement drainage failure, b) cross drainage failure affecting dams, culverts and spillways, and c) river bank erosion; • Structural failures: Collapse of any of the road asset, especially critical assets such as bridges and culverts. While the several hazard mechanisms could trigger failures, the analysis identifies intense rainfall- induced landslides and flooding as the primary cause of road failures in Vietnam. For example, is the analysis recognizes that in Vietnam water is the single biggest trigger of slope movements, since saturation following heavy rain reduces pore suction, adds to the weight of the slope mass, and reduces both cohesion and friction (GITEC 2018). Climate change can amplify the severity of failures due to increased levels of hazard threats. Table 4.1 lists some failure types that can intensify due to climate change.

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Table 4.1. Potential Failure Types Due to Increased Hazard Threats Driven by Adverse Climate Change

Climate Change Impacts

• Damage to roads, under-ground and surface drainage systems due to flooding • Increase in scouring of roads, bridges, culverts, and support structures • Erosion of embankments and road infrastructure due to flooding Increase in rainfall • Triggering of roadside slope failures due to landslides and intensity • Overloading of drainage systems due to increased sedimentation for flooding • Deterioration of structural integrity of roads and bridges due to increase in soil moisture levels • Washout of gravel and earth roads due to flooding • General overtopping of roads, embankments and bridges • Damage to low-lying structures, and bridges due to flooding, inundation Sea level rise in coastal areas, and coastal erosion • Damage to embankments from land subsidence and landslides • Scouring of roads, bridges, and bridge supports • Damage to road pavements and structures from trees and other debris High winds • Damage to bridge decks and suspensions • Impact by wind driven wave action on embankments, bridge abutments • Direct impact erosion of roads, slopes, and embankments from flash Increase in floods typhoon frequency • All risks associated with increase in rainfall intensity due to increase landfall

Sources: Jasper Cook, ReCAP1; ADB 2011; Ebinger and Vandycke 2015; World Bank 2017.

Adaptation Options and Costs In the Vietnam-specific context, the relevant adaptation interventions can be broadly described as a combination of the following:

1. Pavement strengthening: Unpaved roads made of earth and gravel are prone to rapid deterioration due to soil moisture saturation from excess water. Hence, the pavement strengthening option aims to create a more climate-resilient surface by investing in bitumen sealing or concrete paving, which increases the strength and protection of subsurface road conditions to prevent soil saturation. Periodic maintenance of the pavement to preserve its strengthening characteristics should be performed to prevent soil moisture saturation. This intervention protects roads against flooding disruptions. 2. Improved pavement drainage: To avoid disruptive pooling of water and sediments on roads (or even bridges), this investment option is aimed at improving the road surface crossfall, putting more side drains, turn-out drains, and scour checks. Periodic maintenance clears the drainage and prevent scour. This intervention protects roads against flooding disruptions. 3. Earthwork protection: To prevent structural collapse of sloped earthworks, this investment option works by adopting different climate-resilient slope strengthening measures, including improved masonry standards (ADB 2011) to enhance embankments and cut road slopes.

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Periodic maintenance of the slopes should be performed to remove loose materials. This intervention protects the roads against landslide disruptions. 4. Slope protection: Aimed specifically at enhancing climate-resilience of road slope structures, this option includes installing gabions, block facing, bio-engineering of planted vegetation to strengthen slopes (ICEM 2017b), and improving face drainage against overtopping due to river flooding. Investment in slope strengthening can be undertaken with periodic maintenance. This intervention protects the roads against landslide disruptions. 5. Improved cross drainage (culverts): This investment option builds more climate-resilient culverts and/or increases the size and protection standards of existing culverts (stream and relief), reflecting the increases in flood intensities due to climate change. Periodic maintenance of the culverts is performed to remove debris and sediments to prevent scouring. Flood vulnerability of culverts in Vietnam is a major issue, with several instances of culvert and small bridge failures recorded during major flood events (CSCNDPC 2017). This intervention protects the roads against flooding disruptions.

Implementation of the above options creates a climate-resilient road protected against all types of natural hazards risks. Hence, the study suggests a climate-resilient road design, which includes combinations of the five options as described above. In simplistic terms, this study suggests that building a climate-resilient road in Vietnam requires implementing one or more pavement upgrade options, enhancing the pavement drainage, constructing embankments and cut-slopes, increasing slope stability by investing in different measures, and improving cross-drainage by building more culverts.

For the purposes of this study, the analysis generalizes a set of fixed adaptation options across roads, depending on:

• Road class, either to the standard of a climate-resilient national road or to the standard of a climate-resilient district road. • Road terrain, where a sample road is designed either for a flat terrain or a mountain terrain. • Bridge, where a sample bridge is designed to represent any type of bridge in Vietnam.2 In order to enhance a road’s climate resilience, the study selected four climate-resilient road prototype designs: national mountain, national flat, district mountain, district flat, and bridge, identifying every at-risk road associated with one of these prototype designs. For each prototype road, the study designed a sample climate-resilient, 100-meter section by creating a bill of quantities, which specifies the amounts and unit costs of quantities needed to uplift the climate resilience of existing roads. The results of the study’s climate-resilient design assumptions, summarized in table 4.2, arrive at unit costs of investment (CI) in US$ millions per length for upgrading existing national and district roads to prototype climate-resilient road standards.

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Table 4.2. Cost Summary of Initial Adaptation Investment for Building Prototype Climate Resilient Roads in Vietnam

Cost of adaptation investment (CI) Prototype road Terrain (US$/km)

National road Flat 1,535,000 • Two-lane, 22.5 meters wide Mountain 1,828,500

District road Flat 808,000 • One-lane, 6.5 meters wide Mountain 1,439,000

Bridge All 10,179,000

Source: World Bank calculations, with inputs provided by Dr. Jasper Cook, Research for Community Access Partnership (ReCAP): http://research4cap.org.

Results of Adaptation Analysis In estimating the parameters of the adaptation net present value (NPV) calculations, the study assumed the following:

• All climate hazard scenarios models estimated hazard severity until the year 2050.3 Accordingly, the study considers a 35-year timeline of any adaptation intervention, from 2016 to 2050. • The study assumed a discount rate of 12 percent, based on the Vietnam Road Asset Management System (VRAMS) cost-benefit analysis tool assumption. • The study assumed the adaptation options were implemented over the length of the network links affected by hazards. For example, if 10 percent of a road network link was affected by a hazard, then the costs were estimated for this 10 percent section. • The study assumed the freight flow volumes across the networks would grow as per the International Monetary Fund (IMF) forecasted 6.5 percent GDP growth for Vietnam.4 Thus, the study assumed economic impacts would grow by 6.5 percent every year over the timeline of the adaptation option. For each road network under consideration, the study performed adaptation analysis on each link subjected to each hazard model assembled for the study. Thus, if a single road network link is at risk for typhoon flooding, river flooding, and landslides, the study estimates the costs and benefits of adaptation options for each type of hazard risk under each model scenario (current, or climate change RCP4.5 and RCP8.5, with the given time-horizon). For example, if a 1 km national road link is exposed to extreme river flooding over 100 to 500 m of its length, depending upon flooding return periods, then the study estimates the costs and benefits of adaptation for a chosen climate-resilient design for 500 m of this road. If the same road is exposed to extreme typhoon flooding over 100 to 200 m of its length, depending upon typhoon intensities, then the study estimates another set of costs and benefits for building climate resilience over 200 m of the road. Thus, the study estimates multiple climate-resilient designs for the same roads, depending upon their hazard exposures and risks. Table 4.3 lists the overall numbers of unique hazard exposure and adaptation options scenarios calculated for each type of road network.

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Table 4.3. Numbers of Single-Link Failure and Hazard Exposure/Adaptation Options Scenarios Evaluated for Transport Networks in Vietnam

Unique hazard exposure/adaptation Selected transport network Unique single-link failure scenarios options scenarios

National-scale roads 968 6,010

Lao Cai province roads 1,299 5,049

Binh Dinh province roads 11,042 14,397

Thanh Hoa province roads 26,852 38,449

National-scale road network adaptation costs and benefits

This section presents a number of results at the national-scale road network level, discussing the adaptation metrics described in earlier sections. Figure 4.1A shows the maximum initial investments (in US$ millions) required for individual road links to make them resilient to their worst levels of climate hazards. These estimates depend upon the length and width of the road links exposed to the extreme hazards, which implies that some roads require larger investments because of their very large hazard exposures. The results show particular roads links around Ho Chi Minh might requirement as high as an US$55 million investment to make them climate resilient. By adding the maintenance costs to the initial adaptation investments, the results also highlight particular road links around Ho Chi Minh that might require as high investments as US$105 million over 35 years to maintain climate resilience. Figure 4.1B shows the adaptation investment costs per kilometers, which gives a sense of the comparative levels of investments required to uplift roads from current standards to climate-resilient standards. The spatial analysis shows the costs of investments per kilometer of national roads, which can reach as high as US$3.4 million for initial investments and US$6.4 million for investments over time. Due to the study’s assumed chosen options for very high design standards meant to avoid any failures, these investment amounts compare to the construction of a new expressway,

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Figure 4.1. Maximum Total Investment over 35 Years on the Climate Adaptation for Identified Links in the National-Scale Road Network in Vietnam

A. Maximum investment over time per section B. Maximum total investment per km over time

Figure 4.2A shows the maximum values of the estimated benefits over 35 years of investments in climate resilience of individual road links in the national-scale road network. These benefits represent the sum of the avoided annual expected rehabilitation costs over time for individual road links as well as the systemic expected annual economic losses (EAEL) over time from 10-day disruptions of transport freight flows caused by failures of individual road links. The results highlight the large benefits of investing into building climate resilience, particularly along the expressway sections toward the eastern coastline. Figure 4.2B shows the maximum benefit-cost ratios (BCRs) of adaptation for all identified road links, derived from the results of figures 4.1A and 4.2A. The BCRs highlights where climate resilience investments could be prioritized—for road links with high BCRs, much greater than one. The results here show that the expressway link between Nghe An and Thanh Hoa has the highest BCR (197), which makes it a high priority link for climate resilience investments.

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Figure 4.2. Estimated Maximum Benefits over 35 Years and Maximum BCRs of Adaptation Options for Identified Links in the National-Scale Road Network in Vietnam

A. Maximum benefits over time B. Maximum Benefit-Cost Ratio (BCR)

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Another key insight from the adaptation analysis considers how BCRs might change under current and future climate hazard scenarios. This helps make a case for proactively investing in climate resilience for future levels of extreme hazards, rather than planning for current extreme hazard levels. For the national-scale road network links, such a comparison is made between current and future (RCP 4.5 and RCP 8.5) scenarios of river flooding. Figure 4.3A shows the maximum BCRs for current levels of river flooding, while Figures 4.3B and 4.3C show the maximum BCRs for future levels of river flooding under the RCP4.5 and RCP8.5 scenarios respectively. A substantial uplift in the BCRs in the future hazard scenarios makes a strong case for investing into building climate resilience to future hazards in Vietnam. The increase in BCRs is due mainly to the increase in the future river flooding risks under both emission scenarios, showing the greater benefits from investing in climate resilience to protect against potential future higher losses.

Table 4.4 provides the list of specific national road network links identified by their BCRs of adaptation. These links represent the top 20 assets ranked by maximum BCR values, and also report the minimum BCRs. If both the minimum and maximum BCR values are greater than 1, then the case for investing into such assets is quite robust, since every hazard scenario illustrates that investing in climate resilience is more beneficial than paying the adaptation costs. All 20 assets shown have a robust case for investing in climate resilience. The analysis shows that for these 20 assets a cumulative climate adaptation investment amounts to approximately US$95 million initially; over 35 years is the investments would amount to approximately US$153 million. However, the cumulative benefits of such investments over 35 years—estimated by adding together the benefits from individual links—are quite substantial, ranging between US$651 and US$3,656 million. Notably, the combined benefits of investing in all roads concurrently will not necessarily equal the sum of individual benefits because the avoided economic losses are not additive, but rather depend on the estimation of systemic network impacts.

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Figure 4.3. Comparisons of BCRs of Adaptation Options for National-Scale Road Network Links in Vietnam

A. Current 2016 river flooding B. Future 2030 river flooding under RCP 4.5 emission scenarios

C. Future 2030 river flooding under RCP 8.5 emission scenarios

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Table 4.4. Twenty National-Scale Roads Ranked in Descending Order of Maximum Adaptation BCRs

55 55 57 61 60 62 70 62 72 75 85 77 76 94 104 107 119 128 149 197 Max.

BCR 4 2 3 2 3 4 2 3 2 3 2 2 7 8 8 28 29 10 10 11 Min. Max. 13.68 110.81 134.68 159.09 113.90 170.44 131.75 127.84 189.73 192.87 133.13 169.72 171.94 206.74 137.31 319.61 203.75 333.83 320.16 315.87 3,656.85 6.50 9.33 Min. 14.14 31.62 11.02 35.78 23.68 16.15 82.20 71.82 44.28 23.77 23.44 12.84 11.21 29.07 50.30 85.70 36.33 32.57 651.76 Beneft (US$ millions) Beneft 3.32 0.23 3.40 5.80 8.61 4.08 2.50 4.54 8.43 7.77 5.61 2.98 4.45 4.73 4.04 12.72 15.62 30.12 11.67 13.10 153.72 (US$ millions) Total investment investment Total 2.05 7.84 0.15 2.10 9.90 3.57 5.30 2.52 1.59 2.80 7.18 5.19 4.93 3.56 1.79 2.74 8.07 3.00 2.49 18.53 95.30 (US$ millions) Inital investment Inital investment 55 796 1,052 4,055 1,080 6,414 1,849 2,746 1,298 9,609 1,019 1,445 3,721 2,688 3,189 2,303 1,414 4,178 1,943 1,285 (meters) exposure length length exposure Maximum hazard hazard Maximum 2 2 1 2 3 2 2 2 2 3 2 2 2 3 3 1 2 2 3 2 Total class Road Road Route name Route NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border TTH-DNG border - KHA-NTN border TTH-DNG - KHA-NTN border TTH-DNG border - KHA-NTN border TTH-DNG - KHA-NTN border border TTH-DNG - KHA-NTN border TTH-DNG border - KHA-NTN border TTH-DNG - KHA-NTN border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border TTH-DNG border - KHA-NTN border TTH-DNG - KHA-NTN border ven bi ể n Đườ ng b ộ ven NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border NBH-THA border to TTH-DNG to border NBH-THA border 1 1 1 1 1 1 1 1 1 1 1 DBVB 1 1 1 1 1 1 1 1 Natonal Natonal highway

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Province-scale road network adaptation costs and benefits The study also performed an adaptation analysis for each of the three province-scale road networks. In all map figures that follow, an adaptation analysis was performed for each of the links highlighted in black or shades of red—signifying an exposure to hazard. As indicated in the maps, black highlighting represents a failed link with no alternative routes to nearby commune centers, which impairs economic activity. Red highlighting indicates failed links with alternative routes, which maintain access to the nearest commune centers. On the province-scale, identifying some specific assets with high BCRs would help provide a sense of which kinds of assets—national roads, provincial roads, local (commune or district) roads, or bridges—are key to the accessibility province-scale economic activities. The following sections present lists specific assets—showing the asset types, the commune and districts they are located in, their maximum lengths of hazard exposures, the initial and total investments required to achieve climate resilience, and the minimum and maximum benefits and BCRs from investing in those assets.

Lao Cai province-scale roads network

Figures 4.4A through 4.4D show, respectively, the maximum initial investment, the maximum investments over 35 years, maximum values of the estimated benefits over 35 years, and maximum BCRs of adaptation for all identified road links exposed to extreme hazard. The key highlights from these results identify several road links in the province—prominent candidates for adaptation investments—whose failures cut off access to commune centers and hence economic activities. Figure 4.4D shows several such links in the south of the province have BCR > 1, and include significant points in the network (possibly bridges) where the BCRs are highest. Table 4.5 gives the list of the specific road network assets identified by their adaptation of BCRs. The table ranks the top 20 assets by maximum BCR values, while also reporting their minimum BCRs. The results indicate that some local (commune or district) roads and bridges in the provinces have the highest BCRs, making them most significant to the provision of economic activities; consequently, building their climate resilience should be prioritized. In addition, several road links in Lao Cai important for gaining access to economic activities also emerge as priority assets for climate adaptation investments. The analysis shows that for these 20 assets a cumulative climate adaptation investment amounts to approximately US$9 million initially, with a total investment over 35 years of approximately US$12.9 million. The cumulative benefits of such investments over 35 years, estimated by adding the benefits from individual links, range between US$16.4 and US$22.5 million.

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Figure 4.4. Investments, Benefits, and Adaptation BCR Options for Identified Links in the Lao Cai Road Network

A. Maximum initial investment B. Maximum investment over time

C. Maximum benefit over time D. Maximum BCR

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Table 4.5. Top 20 Lao Cai Road Assets Ranked in Descending Order of Maximum Adaptation BCRs 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.5 1.7 1.9 2.0 2.2 5.3 2.4 6.4 7.9 9.4 Max. BCR 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 0.9 0.5 0.6 1.3 0.5 0.7 1.1 2.0 4.9 1.8 8.8 Min. Max. 80,054 423,483 140,403 375,268 766,128 431,644 712,690 565,302 1,352,103 1,007,735 1,075,376 1,081,185 1,451,825 1,518,548 1,555,555 1,337,024 2,668,361 2,827,630 1,342,008 1,758,052 22,470,373 Beneft (US$) Beneft Min. 55,871 48,905 87,584 421,722 139,489 654,402 231,463 392,406 543,361 524,009 1,352,037 1,007,730 1,075,350 1,081,134 1,451,507 1,516,086 1,551,401 1,326,067 1,815,881 1,156,642 16,433,046

(US$) 60,709 92,928 81,388 59,824 783,067 834,174 838,576 328,453 214,609 570,972 345,176 110,696 222,337 1,049,370 1,124,731 1,173,367 1,199,299 1,028,267 1,387,381 1,388,596 12,893,921 Total investment Total (US$) 50,847 77,235 60,139 90,263 50,122 541,065 724,721 575,960 578,966 228,832 776,177 809,385 827,091 710,312 155,697 957,339 999,203 365,277 249,353 161,241 8,989,225 Inital investment Inital investment 15 24 90 32 28 93 15 368 493 392 394 151 530 553 566 483 653 593 100 146 (meters) exposure length length exposure Maximum hazard hazard Maximum Bac Ha Bat Xat Bat Lao Cai Bat Xat Bat Bao Yen Bao Yen Bao Yen Van Ban Van Van Ban Van Van Ban Van Van Ban Van Van Ban Van Van Ban Van Van Ban Van Van Ban Van Bao Thang Bao Thang District name District Muong Khuong Muong Khuong Muong Khuong Tan An Tan Tan An Tan Nam Xe Nam Gia Phu Gia Phu Van Hoa Van Ban Lien Hoa Mac Son Thuy Pho Rang Xuan Hoa Xuan Nam Pung Nam Nam Pung Nam Dan Thang Thuong Ha Thanh Binh Minh Luong Khanh Yen Ha Yen Khanh Muong Khuong Muong Khuong Commune name Total Natonal road Natonal Natonal road Natonal Natonal road Natonal Natonal road Natonal Natonal road Natonal Natonal road Natonal Natonal road Natonal Natonal road Natonal Natonal road Natonal Bridge Bridge Local road Local Natonal road Natonal Local road Local Expressway road Local Local road Local Bridge Bridge Local road Local Asset type Asset Table 4.5. Top 20 Lao Cai Road Assets Ranked in Descending Order of Maximum Adaptaton BCRs Adaptaton of Maximum in Descending Order Ranked Assets 20 Lao Cai Road 4.5. Top Table

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Binh Dinh province roads

The analysis shows that for these 20 assets a cumulative climate adaptation investment amounts to approximately US$1.2 million initially, and over 35 years reaches approximately US$1.6 million (figure 4.5). The cumulative benefits over 35 years, estimated by adding the benefits from individual links, of such investments range between US$14.2 and US$31.4 million (table 4.6).

Figure 4.5. Investments, Benefits, and BCRs of Adaptation Options for Identified Links in the Binh Dinh Road Network

A. Maximum initial investment B. Maximum investment over time

C. Maximum benefit over time D. Benefit-cost ratio

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Table 4.6. Top 20 Binh Dinh Road Assets Ranked in Descending Order of Maximum Adaptation BCRs

7 7 7 8 9 9 11 11 13 13 14 16 16 16 18 20 34 35 41 52 Max. BCR 2 2 2 4 3 3 3 4 4 7 4 5 5 5 6 15 33 20 12 15 Min. Max. 416,993 168,167 708,369 609,252 523,325 324,475 640,684 327,819 708,861 640,231 771,422 325,041 428,047 2,462,332 1,260,278 2,753,177 4,114,864 3,629,342 1,203,717 9,413,243 31,429,640 Beneft (US$) Beneft Min. 55,223 97,273 98,772 95,993 139,776 222,854 288,159 918,272 188,705 204,282 383,299 203,829 368,444 232,847 683,481 124,418 2,487,608 1,271,253 3,496,164 2,670,602 14,231,253 (US$) 63,890 25,425 95,463 73,557 56,983 28,967 56,250 26,076 54,155 45,579 79,412 47,145 17,944 34,232 10,436 273,955 169,528 201,678 107,448 179,470 1,647,594 Total investment investment Total (US$) 8,917 42,295 21,950 62,045 60,388 40,850 24,850 37,516 22,483 45,480 38,456 66,165 39,739 13,555 69,542 29,163 173,697 139,971 128,485 114,594 1,180,139 Inital investment Inital investment 5 6 5 7 8 8 36 56 18 81 31 13 11 20 45 11 63 168 122 108 (meters) exposure length length exposure Maximum hazard Maximum An Lao Tay Son Tay Tay Son Tay Tay Son Tay Tay Son Tay Tay Son Tay Phu Cat Hoai An Hoai An An Nhon An Nhon Quy Nhon Quy Nhon Quy Nhon Quy Nhon Tuy Phuoc Tuy Tuy Phuoc Tuy Tuy Phuoc Tuy Hoai Nhon Hoai Nhon District name District name Tay An Tay An Tan Hoai Tan Tay Xuan Tay Tay Xuan Tay Hoai Son An Nghia Tay Giang Tay Binh Nghi Phuoc My Phuoc My Nhon Hau Nhon Nhon Hau Nhon Cat Chanh Cat Tuy Phuoc Tuy Commune An Hao Tay Ghenh Rang Ghenh Ghenh Rang Ghenh Phuoc Thuan Phuoc Quang Total Asset type Asset Local road Local Bridge Local road Local Bridge Local road Local Natonal road Natonal Bridge Local road Local Bridge Bridge Bridge Bridge Bridge Bridge Local road Local Local road Local Local road Local Bridge Natonal road Natonal Local road Local Table 4.6. Top 20 Binh Dinh Road Assets Ranked in Descending Order of Maximum Adaptaton BCRs Adaptaton of Maximum in Descending Order Ranked Assets 20 Binh Dinh Road 4.6. Top Table

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Thanh Hoa province roads

Figure 4.6 illustrates and table 4.7 lists the top 20 specific road network assets identified by their BCRs of adaptation. All assets have minimum and maximum BCRs > 1, which makes a robust case for investing in their climate resilience. For these 20 assets, a cumulative climate adaptation investment amounts to approximately US$1.5 million initially, with a total of US$2.3million over 35 years. The cumulative benefits of such investments over 35 years, estimated by adding the benefits from individual links, range between US$7.8 and US$23.4 million.

Figure 4.6. Investments, Benefits, and Adaptation BCRs Options for Identified Links in the Thanh Hoa Road Network

A. Maximum initial investment B. Maximum investment over time

C. Maximum benefit over time D. BCR

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Table 4.7. Top 20 Thanh Hoa Road Assets Ranked in Descending Order of Maximum Adaptation BCRs

7 8 8 6 6 6 6 5 6 5 10 10 10 11 12 16 19 33 21 35 Max. BCR 2 3 2 2 5 2 3 2 2 2 2 3 2 5 4 5 6 6 10 10 Min. Max. 251,168 864,780 564,906 522,847 411,658 244,625 502,973 122,988 355,646 514,015 356,369 364,716 559,082 444,398 257,194 576,843 451,068 1,501,439 2,678,809 11,480,906 23,026,429 Beneft (US$) Beneft Min. 75,491 76,539 38,255 74,402 483,486 261,960 170,467 393,965 133,467 154,620 105,024 159,503 109,882 176,400 164,642 131,301 162,858 135,066 771,381 3,981,353 7,760,062 (US$) 25,500 73,172 81,247 64,783 40,046 88,832 23,368 56,869 49,026 70,332 33,167 46,990 27,673 13,499 27,744 13,600 76,306 200,961 109,837 1,150,046 2,272,999 Total investment investment Total (US$) 22,011 91,084 48,101 42,854 51,807 57,897 16,947 33,924 37,903 32,997 46,325 28,290 31,724 23,790 12,182 23,849 12,265 50,062 165,715 724,512 1,554,239 Inital investment Inital investment 5 9 7 6 2 6 2 54 29 42 37 20 52 11 32 27 40 26 44 712 (meters) exposure length length exposure Maximum hazard Maximum Bim Son Nga Son Nga Nga Son Nga Quan Hoa Muong Lat Muong Lat Muong Lat Muong Lat Muong Lat Muong Lat Muong Lat Muong Lat Muong Lat Nhu Thanh Nhu Nhu Thanh Nhu Nhu Thanh Nhu Thanh Nhu Nhu Thanh Nhu Thuong Xuan Thuong Xuan District name District Pu Nhi Pu Nhi Hai Van Hai Van Bac Son Nga Dien Nga Nga Dien Nga Xuan Cao Xuan Muong Ly Trung Son Trung Thanh Tan Luan Thanh Xuan Khang Xuan Xuan Khang Xuan Quang Chieu Quang Chieu Quang Chieu Quang Chieu Quang Chieu Quang Chieu Commune name Total Bridge Urban road Local road Local Bridge Bridge Local road Local road Local Local road Local Local road Local road Local road Local Bridge Local road Local Bridge Local road Local Bridge Bridge Bridge Bridge Asset type Asset Local road Local Table 4.7. Top 20 Thanh Hoa Road Assets Ranked in Descending Order of Maximum Adaptaton BCRs Adaptaton of Maximum in Descending Order Ranked Assets 20 Thanh Hoa Road 4.7. Top Table

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Decision-Making under Uncertainty: Sensitivity of Costs of Adaptation The global sensitivity analysis shown in figure 4.7 demonstrates that the variations in initial investment cost estimates for national roads are most sensitive to the costs of pavement upgrades and improvements in slope stability, accomplished by installing embankment-retaining gabions (figure 4.7A). The initial climate resilience investment costs in the mountainous terrain of Lao Cai (figure 4.7B) are most sensitive to the investment costs of upgrading and protecting embankments, while Binh Dinh (figure 4.7C) and Thanh Hoa (figure 4.7D) are most sensitive to the investment costs of improving slope stability through road embankments, building embankments, and upgrading pavements. The numbers in figure 4.7 show what percentages of costs are driven by variations in different types of investments.

Figure 4.7. Parameter Sensitivity for National- and Province-Scale Roads

A. Parameter sensitivity for national roads B. Parameter sensitivity for Lao Cai

C. Parameter sensitivity for Binh Dinh D. Parameter sensitivity for Thanh Hoa

Code Item Code Item Code Item DT Gravel Line Drain L & R SS3 Concrete/block Face EW1 Sealed Embankment Construction SS4 River bank P EW2 Concrete Cut slope Formation SS1 Concrete FW Cut slope Toe Retaining Gabions SS2 SS6 Embankment Retaining Gabions Bioengineering

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Ranges and uncertainties in adaptation estimates

National-scale roads

This section discusses the ranges and uncertainties in the estimation of adaptation costs and benefits. Figures 4.8A through 4.8D show for individual national-scale road network links the ranges of: (a) initial investment costs, (b) the NPV of total investments over the 35-year planning horizon; (c) the NPV of adaptation benefits due to losses avoided over the 35-year planning horizon, and (d) the BCRs. The variations in cost estimates for links in figures 4.8A and 4.8B are evident because of their exposure to multiple hazards; thus, the analysis estimates costs depending upon the extent of exposure to each hazard. As discussed in the previous section, the variations in initial investment cost estimates are most sensitive to the costs of pavement upgrades and improvements in slope stability through the installation of embankment-retaining gabions. Total investments costs over time are most sensitive to the increases in pavement maintenance costs; in the model, most pavement maintenance costs are far more than other maintenance costs. The results show the highest estimated initial investments for building climate-resilient national-scale road network range from US$52 to US$55 million, with the NPV of total investment over time going up from US$87 to US$105 million. However, these investments might not necessarily be for the same road links. Variations in NPVs of the adaptation benefits shown in figure 4.8C are sensitive generally to the avoided economic losses, and to some extent the costs of rehabilitation. For links with very high NPVs, the benefits are driven mainly by the values of avoided economic losses. The importance of these links to the wider network makes the case for investing in climate resilience. The analysis shows that the highest NPVs of adaptation benefits range from US$118 to US$334 million, substantially larger than the NPVs of total investments. As shown in figure 4.8D, for several links this leads to generally high benefit-cost ratios (BCR >> 1). Significantly, the analysis shows that from the 60th percentile onwards, the minimum and maximum BCRs of several links are both greater than one, indicating that under every extreme climate hazard scenario, investing in the climate resilience of a significant proportion of national-scale roads would be beneficial.

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Figure 4.8. Adaptation Analysis Results for the National-Scale Road Network in Vietnam

A. Range of initial adaptation investments B. Range of total adaptation investments

C. Range of adaptation benefits D. Range of adaptation BCRs

Comparing hazard-specific adaptation BCRs across different scenarios of the same hazard type shows how the hazard uncertainties influence the adaptation analysis outcomes. Such a comparison is made for the case of national-scale road network risks due to river flooding in current conditions and for the future under RCP4.5 and RCP8.5 emission scenarios, as shown in figure 4.9. The results show a significant uplift of BCRs of adaptation when climate resilience investments are made to avoid future levels of extreme river flooding driven risks. The analysis mostly does not show any difference between BCRs of adaptation when evaluated for RCP4.5 and RCP8.5 climate scenario driven river flooding risks, which is reflected through the complete overlap of the curves for these two scenarios. Significantly, in comparison to the current scenarios, large percentiles of road links have BCRs > 1 when adapting to future climate scenarios. This result provides a very compelling case for a need in Vietnam to plan climate resilience investments in anticipation of future climate change driven hazards, when the risks and therefore benefits of avoiding those risks will be much higher.

Figure 4.9. Ranges of Adaptation BCRs for the National-Scale Road Network in Vietnam, Evaluated for Current and Future Extreme River Flooding Scenarios

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Province-scale roads The province-scale analyses show, in comparison to most road assets, a relatively small percentile of road assets have significantly higher adaptation costs. Initial climate-resilience investments for individual road assets reach in excess of US$2 million for 1) approximately the top 10 percentile assets in Lao Cai; and 2) approximately the top 2 to 3 percentile assets in Binh Dinh and Thanh Hoa. Some of these very high value assets might potentially have high benefits; hence, their costs might be justified. The analyses show very little variations of initial and total investment costs in both Lao Cai and Binh Dinh, while the variability is more apparent in Thanh Hoa. The lack of variations in Lao Cai and Binh Dinh stems from the road network assets in both provinces having similar exposure lengths to the various hazards scenarios; therefore, since the costs, which depend upon the lengths of road assets exposed to hazards, are also similar. As discussed in the previous section, the sensitivity of initial climate resilience investment costs in each province are as follows: a) Lao Cai is most sensitive to the investment costs for upgrading and protecting embankments, due to its mountainous terrain; and b) Both Binh Dinh and Thanh Hoa are most sensitive to investment costs for improving slope stability of roads embankments, building embankments, and upgrading pavements.

The ranges of adaptation benefits in the three province-scale analyses also show that for a relatively small percentile of road assets, the benefits of adaptation are much higher than for the rest of the network assets. Initial climate resilience investments for individual road assets exceed US$2 million for: a) approximately the top 5 percentile assets in Lao Cai; and b) around the top 2 to 3 percentile assets in Binh Dinh and Thanh Hoa. The variations in the ranges of benefits of adaptation in the three province-scale analyses arise are mainly sensitive to the changes in the avoided economic impacts of disruptions. For such assets the case for investing in climate resilience is compelling, because the benefits outweigh the costs, as is evident from the BCR plot for the three provinces.

Figure 4.10D shows that in Lao Cai approximately the top 15% percentile of assets (roughly 190 in the Lao Cai network) at risk of hazard have maximum BCR > 1. In Binh Dinh (figure 4.10C) again assets in the top 2 to 3 percentile have maximum BCRs > 1, which amount to roughly 220 to 330 assets in Binh Dinh and 530 to 800 assets in Thanh Hoa. Though a relatively small proportion, these numbers of assets are significant when it comes to prioritizing climate investments. The next section provides more evidence of where and which assets could be further prioritized, based on identifying the locations and types of specific assets.

The results show a clear case of increased BCRs in Lao Cai when comparing current and future river flooding scenarios, as shown in figure 4.10B, where maximum BCRs become greater than one only for future extreme river flooding scenarios. In Binh Dinh and Thanh Hoa, the number of assets with maximum BCRs > 1 in the future river flooding scenarios also show an increase. As presented earlier in the report, all increases arise from increases in future river flooding risks, which avoided economic loss. Landslide susceptibility in Lao Cai is included here as a prominent hazard in the province, for which the underlying hazard data offered some climate change scenario-based information. In the absence of any probabilistic hazard information on the landslide susceptibility much of the current and future adaptation options show similar outcomes. But, significantly, several assets with BCR > 1 create a compelling case for investing in climate resilience.

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Figure 4.10. Adaptation BCRs for Province-Scale Road Networks

A. Range of BCR adaptation to landslide risks B. Range of BCR adaptation to river flooding risks (Lao Cai) (Lao Cai)

C. Range of BCR adaptation to river flooding risks D. Range of BCR adaptation to river flooding risks (Binh Dinh) (Thanh Hoa)

Notes 1. Dr. Jasper Cook, a civil engineer, serves as infrastructure research manager for the Africa Community Access Partnership (AfCAP) and the Asia Community Access Parternship (ASCAP), research programs under the umbrella of the UKAid-funded Research for Community Access Partnership (ReCAP). For more information, visit: http://research4cap.org.

2. Costs estimates for bridges are highly uncertain and depend heavily on the particular bridge being studied. Hence no claim is made here that the bridge costs considered in this study will apply to each and every bridge in Vietnam. The costs are more an indicative of the order to investment that might be required into bridges.

3. The GLOFRIS models estimate the average maximum flood depths from 2010 to 2049, which is used as a representative of a 2030 event. However, these models essentially estimate hazard scenarios to 2049.

4. Data accessed via the International Monetary Fund DataMapper: http://www.imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/ADVEC/WEOWORLD/VNM.

References

ADB (Asian Development Bank). 2011. Guidelines for Climate Proofing Investment in the Transport Sector: Road Infrastructure Projects. Mandaluyong City, Philippines: Asian Development Bank. https://www.adb.org/documents/guidelines-climate-proofing-investment-transport-sector-road- infrastructure-projects.

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CSCNDPC (Central Steering Committee for Natural Disaster Prevention and Control). 2017 (unpublished). Natural Disasters in Vietnam in 2017. Internal report funded by UNDP.

CSIR (Council for Scientific and Industrial Research), Paige-Green Consulting (Pty) Ltd, and St Helens Consulting Ltd. 2018. Climate Adaptation: Risk Management and Resilience Optimisation for Vulnerable Road Access in Africa, Climate Adaptation Handbook. Africa Community Access Partnership (AfCAP) Project GEN2014C. London: Research for Community Access Partnership (ReCAP) for Department for International Development (DFID). http://research4cap.org/Library/CSIR-Consortium-2018-ClimateAdaptation-Handbook-AfCAP- GEN2014C-181130.pdf.

Ebinger, Jane Olga and Nancy Vandycke. 2015. “Moving Toward Climate-Resilient Transport: The World Bank’s Experience from Building Adaptation into Programs.” World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/23685.

ICEM (International Centre for Environmental Management). 2017a. Technical Report 14: Demonstration Effectiveness Audit. Promoting Climate Resilient Rural Infrastructure in Northern Viet Nam Report Series. Prepared for Ministry of Agriculture and Rural Development and Asian Development Bank, Hanoi, Vietnam. http://icem.com.au/portfolio-items/promoting-climate- resilient-rural-infrastructure-in-northern-vietnam-report-series/

———. 2017b. Technical Report 18: Slope Protection Designs and Specifications. Promoting Climate Resilient Rural Infrastructure in Northern Viet Nam Report Series. Prepared for Ministry of Agriculture and Rural Development and Asian Development Bank, Hanoi, Vietnam. http://icem.com.au/portfolio-items/promoting-climate-resilient-rural-infrastructure-in-northern- vietnam-report-series/

———. 2017c. Technical Report 17: Bioengineering–Field Guidelines for Slope Protection. Promoting Climate Resilient Rural Infrastructure in Northern Vietnam Report Series. Prepared for Ministry of Agriculture and Rural Development and Asian Development Bank, Hanoi, Vietnam. http://icem.com.au/portfolio-items/promoting-climate-resilient-rural-infrastructure-in-northern- vietnam-report-series/

MPWT (Ministry of Public Works and Transport, Lao PDR). 2008. (unpublished). Slope Stability Manual. Vientiane: Government of Lao PDR.

GITEC. 2018. Project Preparation Study (PPS) for Rural Infrastructure Programme Phase VI (PPS RIP 6): Guideline in Climate Adaptation for RIP Rural Roads, Lao PDR. https://gitec- consult.eu/index.php/en/projects?view=project&id=69.

Henning, Theuns Frederick Phillip, Susan Tighe, Ian Douglas Greenwood, and Christopher R. Bennett. 2017. Integrating Climate Change into Road Asset Management: Technical Report 2017. Washington, DC: World Bank. http://documents.worldbank.org/curated/en/981831493278252684/Technical-report-2017.

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Chapter 5: Exploring Multimodal Options for National-Scale Transport

This chapter explains how the multimodal transport system was created for this study. Following this, the chapter evaluates the effect of multimodality on failures by exploring modal shifts from road and rail networks.

In the analysis presented to this point, each mode of transport has been assessed individually to understand the impacts of failures. In reality, failures may result in exploring multimodal options for maintaining transport service continuity. Based on the study’s understanding, multimodal switching options have not been well explored in Vietnam. Hence, this chapter explores the possible changes in network failure effects if multimodal options were always available to accommodate for failures along individual modes. The chapter explores the following key questions:

• What are the multimodal transport networks options available in providing continuous service when individual transport modes are damaged or disrupted by external natural hazard shocks? • Are there any gains in terms of decreasing losses due to multimodal options? Switching from one mode to another during failure, via a multimodal connection, presents a potential adaptation option, and where modal switching is a preferred option, its benefits should be compared with the costs involved in facilitating and improving multimodal links and upgrading transport modes. The analysis presented here explores the benefits of multimodality in making informed decisions. The analysis quantifies impacts of multimodality through two metrics:

• Total economic impact metric: The overall economic criticality of network links measured as the sum of their macroeconomic losses and the freight redistribution costs incurred due to their failures • The AADF tonnage shift: The overall systemic shift of tonnage from a disrupted mode to other modes due to multimodal options The key steps in undertaking a multimodal analysis involve:

• Identifying and creating links at all locations where multimodal transfers might be inferred • Estimating the costs of multimodal shifts • Performing the failure analysis, as previously outlined in the criticality analysis, with the change, now that the entire multimodal transport network-of-networks is considered a rerouting option • Estimating the multimodal metrics The multimodal analysis presented here considered only the failures scenarios for the national-scale road and the railway networks.

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Including Multimodal Links and Costs The study created the physical topology of multimodal network for the national-scale analysis, using the national-scale road, railway, inland waterway, and maritime network models. The study assumed the following while creating the multimodal network links:

• To create the port-road and port-rail links, all identified inland and maritime ports were selected as multimodal points and joined to their nearest road and rail nodes. • To create the road-rail multimodal links, all rail stations were selected as multimodal points and joined to the nearest road nodes. • To create a more realistic spatial mapping, only those multimodal links with lengths < 3 kilometers were selected. The multimodal network links represent a notional connectivity between modes, and at best represent the nearest possible accessibility point from one transport mode to another. A sample of the network, zoomed in at a selected location, is shown in figure 5.1.

Figure 5.1. Multimodal Links Created in the Network Model

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Results of Partial Multimodal Shifts in Road Failure Analysis The 968 cases of individual road failures identified in the road criticality analysis were again tested with the multimodal networks. For each failure scenario, the analysis estimated the AADF disruptions, the macroeconomic losses, the rerouting losses, and the overall economic impacts. The results showed quite evidently that because of multimodality, the main effect was seen in terms of the changing redistribution costs, as most cases of single-point failure were eliminated. Hence, understanding the net economic impacts, due to changing rerouting costs, represents the main result here.

To assume a complete shift of road freight toward other modes via multimodal linkages is unrealistic, because networks such as railways have no capacity to accommodate the large volumes of road tonnages. A more feasible and realistic analysis would be to understand cases that explore options for rerouting a small percentage of the disrupted flows from the road to multimodal networks, while rerouting the remaining freight to undamaged roads. In discussing this option here, the study assumes that for each road failure scenario, 90 percent of freight is rerouted along the road network, with the remaining 10 percent rerouted to the least-cost multimodal option—which could be the road network. The result in figure 5.2 shows that even a 10 percent modal shift can help reduce the economic impacts of road failure. For example, key sections of roads show gains, attributed to modal shifts, ranging from US$0.18 to US$0.47 million per day. Also, across the minimum and maximum flow scenarios, the highest losses along the highest flow routes get reduced to US$0.53 to US$1.43 million per day, in comparison to no multimodal rerouting option loss values of US$0.67 to US$1.9 million per day. Notably, this 10 percent shift from road to other modes reduces economic losses by approximately 20 to 25 percent.

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Figure 5.2. Total Economic Impacts of National-Scale Road Links Failures When Considering 90 Percent Flow Rerouting on Roads and 10 Percent Rerouting along Other Networks via Multimodal Links

A. Minimum economic impact B. Maximum economic impact

The chapter next presents the causal effects of the economic gains and losses due to multimodality indicate where the systemic modal shifts take place. Figure 5.3 shows the combined effects of redistribution of 10 percent of flows in terms of AADF tonnages shifts from national-scale roads to other modes—railways, inland waterways, and maritime. In each figure, panel A shows results for the railways and panel B shows the combined inland waterways and maritime. The results reveal that a predominant number of disrupted road failures prefer to be, rerouted via railways because it is often the cheapest and closest option to high-flow road links. The original VITRANSS study estimated railway transport costs in Vietnam to be lower than costs for road transport (JICA et al 2000), and this finding is reflected in this study. Consequently, if transportation assignments were based only on transportation costs, railways would be preferred over roads. For the minimum to maximum flow

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scenarios, the railways witness an added maximum of approximately 8,500 to 10,600 tons per day in systemic flow rerouting. This can lead to an almost doubling of rail tonnage volumes, based on the estimated tonnages in this study. According to existing analysis (Systra 2018), most of the railways currently operate under capacity, for example:

• Along the Hanoi–Ho Chi Minh line, freight uses only 35 to 41 percent of network capacity, with an estimated leftover capacity between 0 and 29 percent, with 0 percent around Saigon. • Along the remaining lines in the north (radiating from Hanoi), freight uses only 11 to 30 percent of network capacity, with an estimated leftover capacity between 48 and 85 percent. Based on the above it would be quite feasible to redistribute road freight toward railways, especially along the northern routes. Moreover, the figure 5.2 result indicates gains in transferring even 10 percent of disrupted freights from road to rail along the Hanoi to Lao Cai route. Between railways and waterways, the preference for railways outweighs that of waterways, except in a small area in the north and a predominant preference in the southern areas that have no railway networks. Given the high volumes transported on southern waterways, the tonnages shown here can be accommodated by their preferred modes without exceeding capacities.

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Figure 5.3. Redistribution of 10 Percent of Maximum Road Tonnage Flows as Added Tonnages onto Rail and Waterway Networks following Road Network Link Disruptions

A. Maximum total tonnage of road transfers B. Maximum total tonnage of road transfers (Railways) (Waterways and Maritime)

Rail Failure Analysis Results

The 164 cases of individual rail failures identified in the rail criticality analysis were also tested, and for each failure scenario, the study estimated the AADF disruptions, the macroeconomic losses, the rerouting losses, and the overall economic impacts. Again, multimodality quite evidently exerted influence in terms of the changing redistribution costs, as mostly cases of single-points failure were eliminated. The model did not find any substantial cases of macroeconomic losses. Accordingly, the analysis demonstrates the net economic impacts, mainly due to changing rerouting costs. Figure 5.4 shows the economic impacts of individual railway link failures corresponding to the minimum and maximum tonnages flows and generalized costs scenarios, where the economic impacts for a link are represented on that specific link. The biggest change here—in comparison to the result in figure 3.2—is that the elimination of all single failure scenarios significantly reduces the economic losses. Also, large sections of the railway network closer to the southern end of Hanoi–Ho Chi Minh line demonstrate several instances where multimodal options result in economic gains, shown as negative values and in the color blue on the maps. In particular, the economic gains can

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reach as high as US$0.02 to US$0.06 million per day, based on the minimum to maximum flow scenarios. In addition, the highest economic losses fall to US$0.05 to US$0.10 million per day, contrasting with the figure 3.2 result, where these links showed economic losses ranging from US$2.3 to US$2.6 million per day for the same failure scenarios. However, in agreement with the result in figure 5.2, as a cheaper option than roads, the multimodal shift along the Hanoi–Lao Cai railway line shows a loss.

Figure 5.4. Total Economic Impacts of Rail Link Failures when Considering Rerouting Options along National-Scale Roads, Inland Waterways and Maritime Networks via Multimodal Links

A. Minimum economic impact B. Maximum economic impact

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Figure 5.5 shows the combined effects of flow redistribution in terms of AADF tonnages shifts from railways and roads to other modes—national-scale roads, inland waterways, and maritime. In each figure, panel A shows the results for the national-scale roads and panel B shows the combined inland waterways and maritime. The results demonstrate that a predominant number of disrupted railway failures would prefer to be rerouted via national-scale roads as the closest option around high-flow railway links. The national-scale roads witness an added maximum of approximately 8,300 to 9,900 tons per day in systemic flow rerouting for the minimum to maximum flow scenarios, largely due to transfers along the Hanoi–Lao Cai route. The waterway networks witness an added maximum of approximately 3,200 to 4,300 tons per day in systemic flow rerouting for the minimum to maximum flow scenarios, mainly along a small northern section.

Since railway flows are much lower when compared to the roads, these transfer volumes can be accommodated by the other networks. Even though the modal shifts from railways to other modes reduce losses, the frequent traffic congestion along alternative road networks could cause costly delays. The road network failure analysis presents a strong case for the benefits of investing in a reliable railway and stronger multimodal options rather than a continued reliance on roads alone.

Figure 5.5. Redistribution of Maximum Tonnage Flows as Added Tonnages onto National-Scale Roads and Waterway (Inland and Maritime) Networks following Rail Network Link Disruptions

A. Maximum total tonnage B. Maximum total tonnage (National-Scale Roads) (Inland Waterways and Maritime)

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References

JICA (Japan International Cooperation Agency), MoT (Ministry of Transport, Socialist Republic of Vietnam), TDSI (Transport Development and Strategy Institute, Vietnam). 2000. The Study on the National Transport Development Strategy In the Socialist Republic Of Vietnam (VITRANSS)— Technical Report No. 3: Transport Cost And Pricing In Vietnam. Tokyo, Japan: JICA, ALMEC Corporation, and Pacific Consultants International. http://open_jicareport.jica.go.jp/pdf/11596814.pdf.

Systra. 2018 (unpublished). Strengthening of the Railway Sector in Vietnam. Report 3.2: Railway Demand Analysis—Freight. Report provided by Systra for the study.

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Chapter 6: Conclusions and Policy Recommendations

As previously stated, this report aims to create a methodological and computational framework for transport risk analysis in Vietnam. Through the successful demonstration of this framework, the study delivered a novel system-of-systems tool for analyzing and prioritizing transport resilience based on spatial criticalities, risks, and adaptation benefits. Hence, for Vietnam stakeholders, the report’s primary recommendation is to recognize the investment value in a detailed spatial risk analysis approach and building on the existing and new knowledge compiled and created through this project. Based on the analysis results and insights of this project, several other recommendations for stakeholders emerge from this study, as discussed below: Increasing Vulnerabilities Due to Climate Change Recommendation: Vietnam’s transport sectors need to be prepared for the increasing intensity and frequency of extreme hazards due to climate change.

The analysis demonstrates that for all transport sectors in Vietnam, extreme hazard exposures to similar hazards over the same spatial scales increase under climate change scenarios. The overall maximum kilometers of national-scale roads, railways, and province-scale road networks exposed to extreme hazard levels increase under all climate scenarios. All major inland, coastal, and air ports are vulnerable to river flooding with increasing frequency, when extreme levels of flooding seen in 1,000- year events could start occurring in five-year events. Prioritizing Transport Network Resilience for Systemic Economic Benefit Recommendation: The systemic understanding of locations whose failures create increasing economic risks presents a strong economic case for investing in building climate resilience of Vietnam’s transport networks.

The model and analysis results most usefully highlight systemic criticalities and hazard-specific, high- risk network locations, which can be prioritized for further detailed investigations into climate resilience. The model results show locations of road networks whose failures can result in very high daily losses of up to US$1.9 million per day, while railway failures can result in losses as high as US$2.6 million per day. The wider implications of macroeconomic losses due to transport disruptions are quite crucial when factoring such losses. Generally, transport analysis ignores such effects, which result in underestimating the impacts of transport disruptions. Comparisons of current and future hazard risks prominently highlight the strong case in Vietnam to invest in building climate-resilient national-scale roads and railways. In addition, the comparisons strongly indicate that access to economic opportunities in several areas of provinces will be severely affected without investment in building climate-resilient roads. The expected annual economic losses—a measure of risks due to transport failures from future climate change-driven river flooding— all significantly increase by at least 100 percent across national-scale roads, railways, and province-scale road networks. All network links with high impacts show these increases.

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Making the Case for Investment in Adaptation to Build Transport Network Resilience Recommendation: Vietnam’s road networks require investments to overhaul existing road assets to higher climate-resilient design standards. Though such investments could be costly, their benefits outweigh the costs for priority network assets, making adaptation investments viable options for roads.

The analysis has demonstrated that the national-scale and province-scale road networks all need adaptation planning to protect against future climate-related hazards. In the Vietnam specific context, the relevant adaptation interventions for roads considered in this study include:

• Pavement strengthening; • Improving pavement drainage; • Earthwork protection; • Slope protection; and • Improved cross drainage (culverts). To build a ‘climate proof’ road, a climate-resilient road design is suggested in this study, which includes combinations of all options.

Prioritization of investments in transport network resilience should be based on evaluation of the costs of investments in the transport network and the benefits yielded by these investments through avoided disruptions. Analysis of costs and benefits of interventions in the transport network should be risk-based, i.e., should consider both the probability and the consequences of transport network failure. This study has provided evidence of all these steps, providing essential evidence for prioritizing interventions.

The analysis shows that the Benefit-Cost Ratios (BCR) of adaptation investments into national-scale roads are mostly greater than one. The analysis suggests that, for some national-scale roads, upgrading to climate-resilient designs could cost up to US$3.4 million per kilometer, compared to the US$1.0 million per kilometer costs of building roads to existing standards. But the high economic benefits of such investments justify the high costs. Similarly, for province-scale road networks a relatively small—but significant—number of assets have BCRs > 1. Many of these are bridges and local roads, crucial for accessing locations of economic activities. For road networks, the increasing BCRs under future climate-hazards scenarios strengthen the case for investing in climate resilience to protect again future climate risks.

However, in some cases, the selected and analyzed adaptation options might not be the best for climate change adaptation, suggesting a need to investigate several other suitable technologies for increasing the resistance of transport infrastructures to hazards. To some extent, options are location specific. For example, two other adaptation options worth considering could include the following:

1. Scour protection of bridge foundations and abutments, and 2. Forecasting and warning systems, which help to avoid vehicle accidents and train- derailing incidents that exacerbate disruptions.

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Given that this report has demonstrated how adaptation options can be considered and has shown in detail how to evaluate their benefits, a similar process can be followed for other types of options not assessed in this study. For example, beyond direct investment in suggest and new climate resilience technologies, adoption of appropriate operation regimes is central to ensuring their effectiveness in the long-term. The costs of operations should be incorporated within the lifetime cost of the asset, ensuring robust cost-benefit adaptation decision appraisal.

Strong Case for Improving Multimodal Transport Linkages and Enhancing Efficiency across Modes Recommendation: Vietnam’s transport networks need to function as integrated multimodal systems, which can be achieved by improving existing multimodal linkages and creating new ones.

This study has shown that the economic impacts of disruptions can be reduced by building additional transport multimodal connectivity. Often known as increasing “redundancy” in the network, this phrase might be interpreted as being wasteful and inefficient, when in fact, providing extra connectivity and capacity will assist the everyday flow of goods and people as well as providing new rerouting opportunities when major disruptions occur. This study shows a very clear benefit of redistributing flows from the road network to the railways and inland and maritime networks. Even a 10 percent shift of road freights, especially to railways, can reduce economic impacts by 20 to 25 percent. Improvements in enhancing railways and waterways efficiency can reduce risks for already congested roads and take advantage of unused capacity in the railways to accommodate the additional modal shift from roads. The availability of multimodal options would significantly reduce potential losses on the railways, and such options should be considered in the future.

Improving Existing Data Gaps for Further Studies Recommendation: Under this study, Vietnam’s transport resilience planning benefits from a system-of-systems transport risk analysis. The study recognizes the extreme importance of increasing capacity through cross-sector, cross-organization collaborations, which will enable the pursuit of further studies and continued efforts to improve the underlying datasets.

As highlighted throughout the report, the study has coped with severely limited data. Some of these limitations exist in assembling and standardizing the following:

• Topological representations of infrastructure networks with proper attributes • Transport flows that represent the latest conditions and trends • Transportation cost assignments reflective of existing conditions • Economic flows data showing current structure of the country and regional economies • Probabilistic hazards at similar spatial resolutions and with Vietnam-specific climate scenarios • Seasonality information of extreme hazards levels and transport network flows • Information on disruption durations and response behaviors • Costs of various adaptation options most relevant to Vietnam

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The study recommends that effort is invested in improving these data gaps in order to improve future studies and more robustly make the economic case for investment in the transport network.

For increasing capacity to standardize and share data, the study notes the extreme importance or creating a cross-sector and cross-organization collaboration, which involves MoT, DRVN, PDoT, CAAV, VINAMARINE, VIWA, TDSI, MARD, MoNRE, General Statistics Office, among other departments.

The study team has set out the process of improving gaps by providing the study model as an open- source resource (https://github.com/oi-analytics/vietnam-transport), and creating a user document that shows the standardized data requirements (https://vietnam-transport-risk- analysis.readthedocs.io/en/latest/). Gaining the most benefit from these innovative new capabilities will require trained personnel in Vietnam. The model developed in this study can be readily adapted to accommodate further improvements. The study has only addressed some of the economic and social functions of transport infrastructure. Notably, the study has not explored the role of transport infrastructure in enabling passenger travel for work or other purposes, or its role in labor market participation. These should be included in future studies, alongside other wider economic and social benefits of transport infrastructure. In these recommendations the study has already identified that the investment prioritization needed to improve resilience of the transport network would require consideration of the costs and effectiveness of alternative interventions, and recommend these studies serve as the next step after this study of transport network vulnerability and risks.

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Appendix A: Methodology for Transport Risk and Adaptation Assessment

Methodological Framework and Implementation Structure Figure A.1 provides a graphical overview of the system-of-systems methodological approach, which consists of the following components explained below. The report chapters present details on the data and models for the following components: A. Hazard assembly: (A-1) Assembling current and future hazard datasets with the spatial extent, magnitudes, and return periods (or probabilities), and extracting those hazards with values above certain thresholds. B. Multimodal transport networks assembly: The aim is to assemble the multimodal transport system-of-systems. The steps toward creating this system-of-systems include (B-1) Collecting Geospatial Information Systems (GIS) data and creating connected network models from such data; (B-2) Identifying locations on the networks and assigning them attributes (e.g., road conditions, type of port, rail station, etc.); (B-3) Identifying key network nodes where freight transport starts (origins) and ends (destination); (B-4) Collecting freight data and integrating it with the network locations; (B- 5) Assembling information on modal split options and generalized cost based performance measures for the multimodal networks; (B-6) Assigning OD flows on the networks based on a least generalized cost criteria to create average annual daily freight (AADF) estimates; and (B-7) At the provincial scales assigning areas of population concentrations to locations of interest via the road network. C. Failure and flow disruption analysis: Following from A and B, the flow disruption analysis involves: (C-1) Intersecting the hazards with the networks to initiate network edge failure conditions; (C-2) Finding all existing disrupted origin-destination (OD) routes; (C-3) Finding rerouting options and redirecting flows toward alternative routes; (C-4) Estimating flow disruptions in terms of freight tonnage lost or passenger counts lost when there are no rerouting options; and (C-5) Estimating changes in performance measures such as generalized cost changes for freight transport or for changing access for to important location in provinces. D. Macroeconomic loss analysis: Following from C, macroeconomic loss involves (D-1) Assembling the regional input-output (IO) datasets to map the trading relationship between provinces; (D-2) Converting the freight tonnage losses to economic flow losses as US$ per day direct economic supply losses and direct economic demand losses; and (D-3) Estimating the indirect economic losses over the multi-regional system. E. Adaptation options analysis: Estimation of Net Present Values (NPVs) of adaptation, which follows D, involves (E-1) Selecting a list of investment strategies for adaptation; (E-2) Assembling data on the costs of damages of assets, the one-time costs and period maintenance costs of different adaptation options; (E-3) Assembling estimates of the discount rates and timelines of adaptation planning; and (E-4) Calculating the NPVs (BCRs) given by Equations (3) through (6). F. Creation of future scenarios: To understand future transport failures and losses, the steps include (F-1) Assembling statistics on future OD growth scenarios based on projected national or regional Gross Domestic Product (GDP) growth; (F-2) Incorporating structural changes to the

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networks (if possible) in terms of changing conditions of links (e.g., increase in paved roads, upgraded rail lines); (F-3) Assembling statistics and estimating the changes in performance measures that determine generalized cost functions in the future; (F-4) Creating modal options for new flow assignments. To understand the systemic nature of disruptions and losses, the analysis is repeated several times for different hazard events. It can also be updated for current and future infrastructure network configurations and climate change driven hazard events to perform several vulnerability, risk, and adaptation assessments. The above methodological framework is consistent with those previously applied to The United Republic of Tanzania (Pant et al 2018b) and United Kingdom (Thacker et al 2017a), to inform infrastructure vulnerability and extreme hazard risk assessment at regional (Pant et al 2018b) and national (Thacker et al 2017b) scales.

Figure A.1. Graphical Representation of Infrastructure System-of-Systems Risk Assessment Framework

Key definitions The framework presents different types of system-of-systems assessments useful for decision- making:

• Criticality assessment: Criticality here is defined as a measure of the transport link’s importance and disruptive impact on the rest of the transport infrastructure (Pant et al 2015). Criticality assessment results in ranking network assets based on their relative impacts on the serviceability of the transport networks (Arga Jafino 2017). • Vulnerability assessment: Vulnerability is defined as the measure of the negative consequences due to failures of transport links from external shock events (Pant et al 2016). Vulnerability assessment is done in the context of natural hazards and results in understanding the relative impacts of hazards on the continued transport availability.

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• Risk assessment: Risk is defined as the product of the probability of a hazard and the consequences of transport link failures. Risk assessment results in understanding the comparative impacts on different hazard frequencies and assigning composite scores to most disrupted transport links. • Adaptation planning: Planned adaptation refers to measures taken to reduce risks. In the context of climate change the planned adaptation seeks to capitalize on the opportunities associated with climate change (Füssel 2007). For transport systems, adaptation planning seeks to identify the assets and locations that could be prioritized for targeted investments to provide maximum benefits in reducing risks.

All the above assessments are done with the aim of understanding the impacts of transport network service flow disruptions due to natural hazards. Network service flow is defined as the general measure of volumes of mobility between network locations over a suitably chosen time frame. The locations between which service flows are measured are called origin-destination (OD) pairs, with the flow being directed from an origin toward a destination. In this study network flows are modeled and measured in terms of:

• Average annual daily freight (AADF) volumes: The estimated tonnage of freight moving on an average day between different network locations. These measures are derived from a model. • Average annual vehicle traffic (AADT) counts: The estimated numbers of commercial vehicles on an average day between different road network locations. These measures are derived from observed data.1 • Net revenue measures: The estimated daily net revenue of goods and services with access to an important location via a transport network. These measures are estimated for understanding local road access to important locations at the province scale. The estimates for network flows are based on performance measures that quantify the criteria used for assigning flows on the networks. In this study, key network performance measures for freight and net revenue flow assignment depend on:

• Identification of key locations and knowledge of spatial patterns of freight transport, for example, mapping and tracking of freight routes between provinces; • Modal splits that indicate how freight transport mode choices are made; and • Generalized cost, which is a composite measure of the transport costs (in US$), expressed as a function of travel time, transport prices, and variability of service. The estimation of network flows disruptions is done via failure analysis. Failure here is defined as the condition where a network node or link has lost complete functionality. This study does not consider the cases where failed network nodes or links might function partially. The rationale for considering complete loss of functionality is that: a) The main goal is to understand how important the uninterrupted functionality of individual links is to the rest of the networks; and b) The interest here is in simulating catastrophic event conditions where there are instances of complete loss of network functionality along affected nodes or links. This study performed failure analyses based on spatially intersecting hazards with network nodes and links, to infer hazard exposures. A hazard signifies an external shock event that initiates failure in the transport networks. Inferring the asset failures criteria due to hazard exposure requires detailed

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physical process-based models of fragility curves2 of assets for given hazard magnitudes, which generally is not possible. For example, the flood fragility of a road will depend upon its physical dimensions, construction standards, material types, scour to flooding, etc. To select sets of failed nodes and links, simplified failure criteria are chosen, where hazard levels above a threshold are catastrophic enough to cause failures.

The characterization of failures within the transportation networks leads to characterizing the disruptions to network flows. Disruptions refer to the change in network flows and performance measures due to the failures of network links. The disruption estimation consists of the following steps: a) Each OD route through failed links is assumed to be no longer available; b) A flow rerouting option for the disrupted OD route is chosen based on the next best route option that gives the least generalized cost estimate; c) The redistributed flows and performance measures are compared with the pre-disruption values. In some instances, no flow-rerouting options exist because physically the OD route through the failed links is the only available option. Such instances are called complete failure scenarios, and individual links causing such scenarios are called single points of failure. The general term a point of failure is defined to signify a location on the transport routes whose failure significantly threatens the flow of essential services. In this study, points of failures’ disruptive impacts are measured as: a) AADF flow losses; and b) daily losses of net revenue. In instances where flow-rerouting options are available, disruptive impacts are measured in terms of the freight redistribution costs, or the difference between the post-disruption and pre-disruption generalized cost estimates of an OD flow. The study assumes the rerouting options are fully utilized, irrespective of the increase in travel distance, time, and cost of rerouting. For the national-scale analysis, the points of failure scenarios may further result in macroeconomic flow losses, due to the AADF flow losses. These macroeconomic losses are estimated through a multiregional economic input-output (MRIO) dataset for disaster loss estimations (Koks and Thissen 2016). By tracking the flow type and industry type of each disrupted OD route, the economic losses from AADF flow losses result in:

• Macroeconomic supply losses occur when the production capacities of industries are diminished due to unavailability of commodities required for production. • Macroeconomic demand losses occur when the final demands for goods of the disrupted industries are diminished, as they cannot reach the markets. The above economic flows losses are considered direct (output) losses that can be described as the economic flow losses which occur to industry sectors directly affected by the disruption of transport flows. In the economic input-output (IO) system these industry sectors are trading with several other industry sectors across regions, which together make up a multi-regional economic system. These trade relationships create backward (supply) and/or forward (demand) linkages, which get disturbed due to direct losses. This results in indirect (output) losses that can be described as the system-wide effects to other firms and industries via backward and/or forward linkages, proceeding to become the ripple effect of the original direct loss (Okuyama and Santos 2014). In this study, the direct and indirect economic losses points of failure scenarios are estimated in US$ per day values, to account for the impact of an AADF tonnage worth of freight flow losses.

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Estimation of adaptation options Adaptation options are defined as the array of strategies and measures available and appropriate for addressing adaptation needs (CSIR et al 2016). Ideally, we should consider several types of adaptation measures, including structural, institutional, and social changes, among others. In this study, adaptation options involve making investments to improve the structural reliability of transport links. The effectiveness of different adaptation options is evaluated and compared through a cost-benefit analysis (CBA), which is a well-established technique to compare the costs of an option with its benefits (OECD 2006). When the adaptation options involve investments to improve the structural reliability of the transport links, then:

1. The benefits of adaptation are the sum of the avoided Expected Annual Damages (EAD) and the Expected Annual Economic Losses (EAEL). EAD signify the direct damage costs incurred due to physical collapse of transport links, while EAEL signify the costs incurred due to transport flow disruptions as described in the previous section. 2. The costs of adaptation are the sum of the capital costs, CI, of an initial investment to implement a chosen option, and further maintenance costs, CP, to preserve the quality of the asset over its lifetime. 3. The value of the adaptation options is estimated as the difference between the benefits and the costs. 4. The benefits of adaptation are assumed to last over a long time period; thus, the actual value of an adaptation option is estimated over an annual time-scale t0,…,tT, starting at the time t0 when the option is implemented and continues over its planned time horizon T. This leads to the Net Present Value (NPV) of adaptation given as:

NPV= j=0j=TEADtj+EAELtj–CPtj1+rj–CIt0 (3)

Where EADtj is the expected annual damages for year tj, EAELtj, is the expected annual economic loss for year tj, CIt0, is the adaptation investment made at the start year t0, CPtj, is the cost of periodic investment needed,3 r is the discount rate, and j is the count for the years over which the value of adaptation is evaluated.

5. The metric of benefit-cost ratio (BCR) can similarly be estimated to evaluate the effectiveness of the adaption options:

BCR=j=0j=TEADtj+EAELtj1+rjj=TCPtj1+rj+CIt0 (4)

The practical steps in estimating the Net Present Value (NPV) of adaptation involve:

• Having a sample of probabilistic hazard events; • Getting data on the costs of asset damages, the one-time costs of the adaptation option, and the periodic costs of maintenance; and

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• Estimating the economic losses due to transport link failures in two ways, depending upon the scale of the analysis. In both cases the economic loss estimates are daily estimates, which are multiplied by an assumed duration of disruption: a. For the national-scale network analysis, the economic losses are given as sum of the daily macroeconomic losses and the total costs of rerouting. b. For the province-scale network analysis, the economic losses are given as sum of the loss of daily net revenue and the total costs of rerouting. The process of estimating the NPV (or BCR) of an adaptation option is undertaken for all the failed transport links, following which all NPV (and BCR) estimates can be ranked. The links with the highest BCR values should be prioritized for investments, as they provide the highest benefits toward maintaining the network reliability.

A similar process can be repeated for a set of adaptation options, as there are potentially several different adaptation options worth considering. Hence, the NPVs could be compared across all assets and all options to select the ones with high benefits for the system. When considering climate change, the effectiveness of the same set of adaptation options can be further compared between current hazard scenarios and future climate change-driven scenarios.

Uncertainty analysis Previous sections outline several sources of uncertainty throughout the whole process of the criticality, vulnerability, risk and adaptation assessments. Some of these, among others, include: • Uncertainties associated with the estimation of the projected natural hazards under different climate change scenarios; • Aleatory uncertainties (Bae et al 2004) in the network model structures due to lack of proper topological network data; • Epistemic uncertainties4 in the transport flow estimation models, due to imprecise information on the network speeds, costs, and tonnages; and • Uncertainties in the estimation of the costs of asset damages, costs of the adaptation option and the periodic costs of maintenance.

To overcome the challenges of accounting for the different types of uncertainties, this study adopts a robust decision-making approach by simulating hundreds to thousands of different sets of transport link failures under different current and future hazards to describe how adaptation options might perform under many plausible failure scenarios (Lempert et al 2013). At every step the sensitivity of the model outputs to input parameters are analyzed to understand which parameters might influence the estimates the most. This approach aligns with the Decision Making Under Deep- Uncertainty (DMDU) methods, which are gaining popularity among decision-makers (Hallegatte et al 2012; Espinet et al 2018). The robust estimates of the NPVs (or BCRs) of different adaptation options for different transport link failures are created by calculating the minimum and maximum NPV for each type of option for each type of failure scenario.

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To arrive at these estimates, the study assumes that:

• For every adaptation option, the costs of adaptation and maintenance are given or estimated over a range; • The damage costs of assets are given or estimated over a range; and • The economic losses are estimated by accounting for the range of possible tonnages, speeds, and costs along the networks. The study concludes that transport links for which adaptation options give both NPVmin>0 (or BCRmin>1��m) and ��max>0NPVmax>0 (or BCRmax>1��max>1), should be treated as no-regret options and prioritized for investments. Further prioritization of such transport links can be done by ranking BCRs and selecting those with the highest values as these give the highest benefit in terms of improving transport reliability.

Notes

1. No AADT measures are available for other modes of transport.

2. Fragility curves quantify the conditional probability of failure of an asset for a given level of hazard. Fragility curves that are derived from statistical analysis of historical failure data that record the condition of an asset, its level of hazard exposure. They can also be created by simulating the physical properties of a system based on its design standards and stress testing it under different hazard levels.

3. Maintenance could be considered to be part of the benefits as well, if we were considering partial failures. However, used here, maintenance represents a cost option undertaken to prevent the catastrophic failure of an asset.

4. See note no. 3.

References

Arga Jafino, Bramka. 2017. Measuring Freight Transport Network Criticality: A Case Study in Bangladesh. Delft, Netherlands: TU Delft (Delft University of Technology). http://resolver.tudelft.nl/uuid:0905337b-cdf7-4f6e-9cf6-ac26e4252580.

Bae, Ha-rok, Ramana V. Grandhi, and Robert A. Canfield. 2004. “Epistemic Uncertainty Quantification Techniques Including Evidence Theory for Large-Scale Structures.” Computers & Structures 82 (13-14): 1101–12.

Espinet, Xavier, Julie Rozenberg, Kulwinder Singh Rao, and Satoshi Ogita. 2018. “Piloting the Use of Network Analysis and Decision-Making under Uncertainty in Transport Operations: Preparation and Appraisal of a Rural Roads Project in Mozambique Under Changing Flood Risk and Other Deep Uncertainties.” Policy Research Working Paper 8490, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/787411529606457222/.

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Füssel, Hans-Martin. 2007. “Adaptation Planning for Climate Change: Concepts, Assessment Approaches, and Key Lessons.” Sustainability Science 2 (2): 265–275. doi: 10.1007/s11625-007- 0032-y.

Hallegatte, Stephane, Ankur Shah, Robert Lempert, Casey Brown, and Stuart Gill. 2012. “Investment Decision Making under Deep Uncertainty: Application to Climate Change.” Policy Research Working Paper 6193, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/194831468136208564.

Koks, Elco E., and Mark Thissen. 2016. “A Multiregional Impact Assessment Model for Disaster Analysis.” Economic Systems Research 28 (4): 429–449. doi: 10.1080/09535314.2016.1232701.

CSIR (Council of Scientific and Industrial Research), Paige-Green Consulting (Pty) Ltd, and St. Helens Consulting, Ltd. 2016. Climate Adaptation: Risk Management and Resilience Optimisation for Vulnerable Road Access in Africa—Climate Threats Report. Africa Community Access Partnership (AfCAP) Project GEN2014C. London: Research for Community Access Partnership (ReCAP) for Department for International Development (DFID). http://research4cap.org/Library/CSIR- Consortium-2016-ClimateChangeAdaptation-ClimateThreatsReport-AfCAP-GEN2014C- 160908.compressed.pdf.

Okuyama, Yasuhide and Joost R. Santos. 2014. “Disaster Impact and Input–Output Analysis.” Economic Systems Research 26 (1): 1–12. doi: 10.1080/09535314.2013.871505.

Pant, Raghav, Elco E. Koks, Tom Russell, and Jim W. Hall. 2018a. Transport Risks Analysis for The United Republic of Tanzania: Systemic Vulnerability Assessment of Multi-Modal Transport Networks. Final Report Draft. Oxford Infrastructure Analytics Ltd., Oxford, UK. doi: 10.13140/RG.2.2.25497.26722

Pant, Raghav, Scott Thacker, Jim W. Hall, David Alderson, and Stuart Barr. 2018b. “Critical Infrastructure Impact Assessment Due to Flood Exposure.” Journal of Flood Risk Management 11 (1): 22–33 doi: 10.1111/jfr3.12288.

OECD (Organisation for Economic Co-operation and Development). 2006. Cost-Benefit Analysis and the Environment: Recent Developments. Paris: OECD. doi: 10.1787/9789264010055-en

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Thacker, Scott, Raghav Pant, and Jim W. Hall. 2017a. “System-of-Systems Formulation and Disruption Analysis for Multi-Scale Critical National Infrastructures.” Reliability Engineering and Systems Safety 167: 30–41. doi: 10.1016/j.ress.2017.04.023.

Thacker, Scott, Stuart Barr, Raghav Pant, Jim W. Hall, and David Alderson. 2017b. “Geographic Hotspots of Critical National Infrastructure.” Risk Analysis 37 (12): 2490–2505, doi: 10.1111/risa.12840.

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Appendix B: Overview of Datasets

Each transport mode in this study first represented as a network, which is the collection of nodes joined together by a collection of links. The physical network topology defined as the structure that encodes the physical placement of nodes and their connecting links describes the network structure. The existence of physical links with information about their connecting node locations is a necessary and essential condition for the creation of the transport network models, because of the geospatial nature of the transport systems. In the absence of such information the underlying infrastructure data has to be processed further by either adding new link geometries to connect nodes or by filling gaps in missing physical link geometries to connect nodes and thereby complete the physical network topology. All network models were created from geospatial data that needed post-processing to fill gaps in the underlying raw datasets (table B.1). Depending upon the quality of the received raw datasets, the network models represent the physical properties such as the lengths, conditions, and widths of transport network assets as approximations of the equivalent real-world systems.

Table B.1. Raw Datasets Collected from Different Data Sources for the Transport Risks Analysis Modeling in Vietnam

Dataset Data scope Source Year

Administrative boundary Commune, district and GSO, WHO 2016 and statistics province levels

Economic input-output data National scale GSO 2012

National roads TDSI 2018

Province roads Vietbando 2018

Rail VITRANSS II and MoT 2009–2016

Inland waterway National, province VIWA 2018 (roads only) Ports – VINAMARINE Maritime 2018 Routes – WHO

Airports – CAA Airports 2018 Routes – OIA modeled

Inter-province ODs VITRANSS II 2009 OD flows Crop production locations IFPRI 2016

Transport VITRANSS II reports National 2009–2016 Costs (projected to 2016 values)

Adaptation costs Roads Jasper Cook, VRAMS, PMU6 2018

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Natural hazards dataset Table B.2 provides the list of all assembled natural hazard datasets used in this study. From left to right, the table gives the: • Names of the hazard types; • Shorthand model names, either given in the data source or created for the study; • Assumed year of hazard represented by the model; • Climate scenarios; • Hazard probabilities (if any); • Selected banded values for banded hazard datasets for sampling hazard extremes; • Selected flood-depth threshold for all flood datasets for sampling extreme flooding scenarios; • Spatial resolution in terms of the grid size unit areas in the model; • Provinces covered by the hazard maps; and • Source of the datasets.

Ideally, for a natural hazard risk assessment the hazard datasets should contains at least the following information for each type of natural hazard: a) spatial extent; b) magnitude of severity; and c) probability. Unfortunately, only the fluvial flood maps provided for this study contained all three of the above attributes. The typhoon-induced flood maps contained only the spatial extent and magnitudes (flood depths) of flooding, and no probabilities. In the flashflood and landslide susceptibility maps, likelihood banded-scores (representing high or very high susceptibility) over areas were used as proxies for serve flashflood and landslide events respectively, while there was no probabilistic information.

Some of the natural hazards further maps were also provided with future climate change model outputs based on the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios (Meinshausen et al 2011). RCP4.5 scenarios assume that global emissions peak around 2040 and then decline, while RCP8.5 scenarios assume no decline in global emissions through the 21st century. Flashflood and landslide susceptibility maps for eight northern provinces generated for RCP4.5 and RCP8.5 scenarios in 2025 and 2050 were available for the study. Fluvial flood maps for the whole country were also provided for current flooding and future flooding under RCP4.5 and RCP8.5 scenarios in 2030, from the GLOFRIS (Winsemius et al 2013) model. In addition, the study looked at a baseline model presenting current flooding conditions, called EUWATCH. Also, the study considered flooding outcomes under different climate change scenarios, using bias-corrected future meteorological data for an ensemble of five Global Climate Models (GCMs) in the CMIP5 project – GFDL-ESM2M,1 HadGEM2-ES,2 IPSL-CM5A-LR,3 MIROC-ESM-CHEM,4 and NorESM1-M.5 All the current and future flood maps were derived by downscaling global distributed hydrological models with a resolution of 0.5° × 0.5° (approximately 50 m × 50 km at the equator) to 1/120° × 1/120° (900 m × 900 m around the equator) to give country scale maps. Using these models, the future flood maps showed average maximum flood depths from 2010 to 2049, which in this study are assumed to represent flood scenarios for the year 2030. For each model output, nine different flood intensity maps were produced to represent fluvial floods that could occur every: 2, 5, 10, 25, 50, 100, 250, 500, and 1,000 years.

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Table B.2. Natural Hazard Datasets Assembled for the Study Notes

e h i b 2014 Source GLOFRIS MARD 2016 MARD 2017 MARD 2012 VIGMR-MONRE VIGMR-MONRE c g a d Spatal Spatal 8 Central 8 Central coverage provinces provinces 28 Coastal 28 Coastal provinces Thanh Hoa Thanh Hoa 15 Northern 15 Northern Whole country Whole Spatal Spatal 30m×30m 30m×30m resoluton 100m×100m 900m×900m — >= 1m (meter) Flood depth Flood depth — 4-high, 3-high, 5-very high 5-very 4-very high 4-very Banded values Banded — 0.2 0.2, 0.5 0.2, 0.004, 0.01, 0.01, 0.004, Probabilites 0.001, 0.002, 0.001, 0.02, 0.04, 0.1, 0.1, 0.02, 0.04, 0.01, 0.05, 0.1, 0.1, 0.01, 0.05, — — — — — Climate Climate scenarios RCP4.5, RCP8.5 RCP4.5, RCP4.5, RCP8.5 RCP4.5, RCP8.5 RCP4.5, Year 2016 2016 2016 2030 2050 2016 2025 2050 2016 2025 2016 2016 f MARD IMHEN WATCH MoNRE NorESM1-M Model name HadGEM2-ES GFDL-ESM2M IPSL-CM5A-LR MIROC-ESM-CHEM MARD Level 13 – 16 MARD Level

Landslide susceptbility Hazard type Hazard Fluvial fooding Flash food Flash food susceptbility Typhoon fooding Table B.2. Natural Hazard Datasets Assembled for the Study Assembled for Datasets Hazard B.2. Natural Table : Notes and Quang Binh. Da Nang, Qunag Nam, Binh Dinh, Quang Tri, Thua Thien Hue, of Phu Yen, a. Provinces Resources of Natural Ministry Resources, and Mineral of Geosciences Insttute Vietnam the in 2014 by conducted mitgaton disaster for in Vietnam areas mountainous for and zonaton assessment investgaton, b. Landslide and Environment. = Band 4. = Band 5; and (2) Orange rules: (1) Red on following based values, integer to translated which were bands, color-coded contained original dataset c. The Bac Giang, Tha Nyugen, and Lang Son. Cao Bang, Quang, Bac Kan, Ha Giano, Tuyen Phu Tho, Hoa Binh, Son La, Bai, Yen Bien, Dien Lai Chau, of Lao Cai, d. Provinces Vietnam. Development, and Rural of Agriculture the Ministry in 2017 by and ADB completed UNDP funded by project Nam Viet in Northern Infrastructure Rural Resilient Climate e. Promotng hour; (3) 15: 167–183 km per 150–166 km per hour; hour; (2) 14: (1) 13: 134–149 km per levels: diferent for winds speeds with following classifcaton, wind speeds based on the Beaufort by determined are levels Typhoon f. hour. and (4) 16: 184–201 km per of the country. coastline Eastern the Quang Ninh touching Thuan to Ninh from g. All provinces Vietnam. Development, and Rural of Agriculture Ministry Ofce, Project Central the in 2012 by Bank and completed the World by funded Project Risk Management Disaster h. Integrated al 2013. i. Winsemius et

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Notes 1. From NOAA in US: https://www.gfdl.noaa.gov/earth-system-model/.

2. From UK Met Office: https://portal.enes.org/models/earthsystem-models/metoffice-hadley- centre/hadgem2-es. 3. From IPSL in France: http://icmc.ipsl.fr/index.php/icmc-projects/icmc-international- projects/international-project-cmip5. 4. From Japan: Watanabe, Mashahiro, Tatsuo Suzuki, Ryouta O’ishi, Yoshiki Komuro, Shingo Watanabe, Seita Emori, Toshihiko Takemura, Minoru Chikira, Tomoo Ogura, Miho Sekiguchi, Kumiko Takata, Dai Yamazaki, Tokuta Yokohata, Toru Nozawa, Hiroyasu Hasumi, Hiroaki Tatebe, and Masahide Kimoto. 2010. “Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate Sensitivity.” Journal of Climate 23: 6312–35. doi: 10.1175/2010JCLI3679.1.

5. From Norway: Bentsen, M., I. Bethke, J. B. Debernard, T. Iversen, A. Kirkevåg, Ø. Seland, H. Drange, C. Roelandt, I. A. Seierstad, C. Hoose, and J. E. Kristjánsson. 2013. “The Norwegian Earth System Model, NorESM1-M—Part 1: Description and Basic Evaluation of the Physical Climate.” Geosci Model Dev 6 (3): 687–720. doi: 10.5194/gmd-6-687-2013

References Meinshausen, M., S. J. Smith, K. Calvin, J. S. Daniel, M. L. T. Kainuma, J-F. Lamarque, K. Matsumoto, S. A. Montzka, S. C. B. Raper, K. Riahi, A. Thomson, G. J. M. Velders, D.P. P. van Vuuren. 2011. “The RCP Greenhouse Gas Concentrations and their Extensions from 1765 to 2300. Climatic Change 109 (1-2): 213–241. doi: 10.1007/s10584-011-0156-z.

Winsemius, H. C., L. P. H. Van Beek, B. Jongman, P. J. Ward, and A. Bouwman. 2013. “A Framework for Global River Flood Risk Assessments.” Hydrology and Earth System Sciences 17 (5): 1871–92. doi: 10.5194/hess-17-1871-2013.

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Appendix C: Vulnerability Results for Ports

For the air transport, inland waterway, and maritime sectors, the report presents results of the criticality and vulnerability assessment performed at the major ports scales, as these are the most important assets on these networks. Here, the following section presents the summary tables for each sector showing the name and location of a port, the minimum and maximum average annual daily freight (AADF) daily tonnage at the port that could potentially be vulnerable to disruptions, the type of hazard that could cause said disruption, the climate scenario for the hazard, and the minimum and maximum probabilities of hazard exposure of the port. For each sector, the risks of hazard exposures due to climate change will increase. Every table shows that wherever the identified hazard has climate scenarios with probabilities, the maximum probabilities of exposure under the climate scenario is greater than the current probability of exposure. For example, in Table C.1 the maximum hazard probability of flooding in Ho Chi Minh port increases to 0.2 (1 in five-year flooding) under the RCP4.5 and RCP8.5 scenarios, in comparison to the 0.04 (1 in 25-year flooding) probability at present. This means that the Ho Chi Minh port is about 5 times more prone to frequent flooding in the future than at present. Similar trends are observed for all the major ports in every sector, including maritime (table C.2) and inland waterways (table C.3).

Table C.1. Airports with AADF Flows and Hazard Exposure Scenario Results

AADF Probability (tons/day) Climate Name Commune District Province Hazard type scenario Min Max Min Max

Landslide — — — Hoa Thuan Hai Đà Nẵng Da Nang 26.0 28.3 Tay Chau Typhoon — — — Flooding Hai Typhoon Cát Bi Thanh To Hai An 5.6 5.8 — — — Phong Flooding

— 0.001 0.002 River RCP 4.5 0.001 0.01 Flooding Thua Huong Phú Bài Phu Bai Thien 2.8 2.8 RCP 8.5 0.001 0.01 Thuy Hue Landslide — — —

Typhoon — — — Flooding

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Table C.2. Major Maritime Ports with AADF Flows and Hazard Exposure Scenario Results

AADF (tons/day) Hazard Climate Probability Name Commune District Province Min Max type scenario Min Max

— 0.001 0.04

TP. Hồ Ho Chi River Cat Lai Quan 2 84,050.0 106,425.6 RCP 4.5 0.001 0.2 Chí Minh Minh Flooding

RCP 8.5 0.001 0.2

Hải Ngo Typhoon May To Hai Phong 80,688.2 101,739.6 — — — Phòng Quyen Flooding

— 0.001 0.04

Binh River Cần Thơ Binh Thuy Can Tho 68,863.8 86,686.3 RCP 4.5 0.001 0.2 Thuy Flooding

RCP 8.5 0.001 0.1

— 0.001 0.001 Long Binh River Đồng Nai Bien Hoa Dong Nai 27,534.1 29,102.7 Tan Flooding RCP 4.5 0.1 0.1

Thanh Typhoon Nghi Sơn Nghi Son Tinh Gia 18,140.3 18,822.4 — — — Hoa Flooding

Quy Quy Hai Cang Binh Dinh 4,917.8 5,376.7 Landslide — — — Nhơn Nhon

Landslide — — — Thuận Phu Thua Thuan An 1,105.6 2,893.8 An Vang Thien Hue Typhoon — — — Flooding

Typhoon Cửa Lò Nghi Thuy Cua Lo Nghe An 1,428.6 2,021.0 — — — Flooding

Typhoon Bến Thủy Nghi Hoa Cua Lo Nghe An 1,131.2 1,655.5 — — — Flooding

— 0.001 0.04

Phuoc Tan Ba Ria — River Cái Mép 416.8 416.8 RCP 4.5 0.001 0.1 Hoa Thanh Vung Tau Flooding

RCP 8.5 0.001 0.1

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Table C.3. Major Inland Waterway Ports with AADF Flows and Hazard Exposure Scenario Results

AADF (tons per day) Hazard Climate Probability Name Commune District Province Min Max type scenario Min Max — 0.001 0.04 Cảng Bình River Binh Long Chau Phu An Giang 24,681.4 55,018.2 RCP 4.5 0.001 0.2 Long Flooding RCP 8.5 0.001 0.1

Cảng Thủy Thuy Typhoon nội địa Hà Gia Duc Hai Phong 32,265.7 40,662.7 — — — Nguyen Flooding Hùng Anh

Cảng Nhiệt Typhoon Điện Thái My Loc Thai Thuy Thai Binh 36,441.7 37,206.7 — — — Flooding Bình 2

Cảng TNĐ Liên hiệp Khoa học Công nghệ Typhoon Tài nguyên Dien Cong Uong Bi Quang Ninh 33,948.1 34,822.8 — — — Flooding khoáng sản, môi trường và năng lượng

— 0.001 0.04 CẢNG LONG Ho Chi River Long Binh Quan 9 25,214.8 31,927.5 RCP 4.5 0.001 0.2 BÌNH Minh Flooding RCP 8.5 0.001 0.2 — 0.001 0.01 Cảng Khuyến River Yen So Hoang Mai Ha Noi 18,291.7 18,552.1 RCP 4.5 0.001 0.02 Lương Flooding RCP 8.5 0.001 0.02

Cảng Bốc xếp Vinh — 0.001 0.04 River hàng hóa An Thanh Chau Phu An Giang 6,174.6 14,637.2 RCP 4.5 0.001 0.2 Flooding Giang Trung RCP 8.5 0.001 0.1

Cảng Thủy — 0.001 0.004 River nội địa Long Long Duc Tra Vinh Tra Vinh 3,257.6 14,515.1 RCP 4.5 0.001 0.04 Flooding ứ Đ c RCP 8.5 0.001 0.04 Typhoon An Hòa An Hong An Duong Hai Phong 8,064.4 10,160.4 — — — Flooding

CẢNG TNĐ — 0.001 0.04 Truong Ho Chi River KHO VẬN Thu Duc 7,553.0 9,560.9 RCP 4.5 0.001 0.2 Tho Minh Flooding Ề MI N NAM RCP 8.5 0.001 0.2 — 0.001 0.01 River Phuong 5 Vinh Long Vinh Long 8,051.3 9,080.0 RCP 4.5 0.001 0.04 Flooding RCP 8.5 0.001 0.04 — 0.001 0.02 CẢNG TNĐ River Long Tan Nhon Trach Dong Nai 8,320.2 9,022.3 RCP 4.5 0.001 0.04 TÍN NGHĨA Flooding RCP 8.5 0.001 0.1

Table contnues on next page

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Table C.3 continued

AADF (tons per day) Hazard Climate Probability Name Commune District Province Min Max type scenario Min Max

XÓA TÊN KHỎI DS River — 0.001 0.001 (CẢNG TNĐ An Binh Bien Hoa Dong Nai 8,261.4 8,747.6 Flooding VẬN TẢI SONADEZI) RCP 4.5 0.1 0.1 — 0.001 0.01 Cảng Phượng Cam River Hai Duong Hai Duong 6,186.8 6,226.3 RCP 4.5 0.001 0.02 Hoàng Thuong Flooding RCP 8.5 0.001 0.02 — 0.001 0.04 CẢNG HÙNG River Thien Tan Vinh Cuu Dong Nai 5,526.7 5,949.2 RCP 4.5 0.001 0.2 TÀI Flooding RCP 8.5 0.001 0.2

CẢNG TNĐ — 0.001 0.001 XĂNG DẦU Long Binh River Bien Hoa Dong Nai 5,516.1 5,879.8 LONG BÌNH Tan Flooding RCP 4.5 0.1 0.1 TÂN

— 0.001 0.01 Cảng Hồng River Hong Van Thuong Tin Ha Noi 4,585.8 4,690.7 Vân Flooding RCP 4.5 0.001 0.02 RCP 8.5 0.001 0.02 CẢNG TNĐ River — 0.001 0.001 An Binh Bien Hoa Dong Nai 4,402.2 4,651.0 AJINOMOTO Flooding RCP 4.5 0.1 0.1

Cảng thủy NONE 0.001 0.001 River nội địa Vissai Gia Tan Gia Vien Ninh Binh 3,392.6 3,762.9 RCP 4.5 0.001 0.002 Flooding Gia Tân RCP 8.5 0.001 0.004 Cảng thủy River — 0.001 0.001 Kim Xuyen Kim Thanh Hai Duong 3,705.2 3,715.1 nội địa CuBi Flooding RCP 8.5 0.001 0.001

CẢNG TNĐ — 0.001 0.04 River HOÀNG Thien Tan Vinh Cuu Dong Nai 3,301.7 3,488.7 RCP 4.5 0.001 0.2 Flooding LONG RCP 8.5 0.001 0.2

CẢNG TNĐ — 0.001 0.02 BÊ TÔNG RCP 4.5 0.001 0.1 Luong River LY TÂM LA Ben Luc Long An 2,237.7 2,673.5 Binh Flooding ƯƠ PH NG RCP 8.5 0.001 0.1 NAM

— 0.001 0.01 Cảng Binh River Thanh Tri Hoang Mai Ha Noi 2,282.2 2,310.8 RCP 4.5 0.001 0.02 Đoàn 11 Flooding RCP 8.5 0.001 0.02 Cảng thủy — 0.001 0.01 nội địa xi Thanh River Thanh Liem Ha Nam 1,905.3 1,910.0 RCP 4.5 0.001 0.02 măng Xuân Nghi Flooding Thành RCP 8.5 0.001 0.02

Cảng Thủy — 0.001 0.01 River Nội Địa Cống Ngoc Son Tu Ky Hai Duong 1,855.1 1,870.9 RCP 4.5 0.001 0.02 Flooding Câu RCP 8.5 0.001 0.02

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Table C.3 continued

AADF (tons per day) Hazard Climate Probability Name Commune District Province Min Max type scenario Min Max

— 0.001 0.01 Cảng thủy nội Thanh River địa Vissai Hà Thanh Liem Ha Nam 1,435.2 1,456.7 Thuy Flooding RCP 4.5 0.001 0.02 Nam RCP 8.5 0.001 0.02

Cảng xi măng — 0.001 0.01 River Vicem Bút Chau Son Phu Ly Ha Nam 1,429.5 1,435.6 Flooding Sơn RCP 4.5 0.001 0.02 RCP 8.5 0.001 0.02 — 0.001 0.04 CẢNG TNĐ Phuoc River VIỆT HÓA Can Duoc Long An 978.3 1,214.8 RCP 4.5 0.001 0.1 Dong Flooding NÔNG RCP 8.5 0.001 0.1 — 0.001 0.02 Cảng hàng River hóa Thành My Khanh Phong Dien Can Tho 801.1 1,088.8 RCP 4.5 0.001 0.04 Flooding Hưng RCP 8.5 0.001 0.04 Cảng Trường Thuy Typhoon Gia Minh Hai Phong 797.0 1,000.3 — — — Nguyên Nguyen Flooding

Cảng thủy — 0.001 0.01 nội địa Nhà Thanh River Thanh Liem Ha Nam 859.0 866.0 RCP 4.5 0.001 0.02 máy xi măng Nghi Flooding Thành Thắng RCP 8.5 0.001 0.02 — 0.001 0.01 Cảng thủy nội Thanh River địa xi măng Thanh Liem Ha Nam 856.4 856.6 RCP 4.5 0.001 0.02 Nghi Flooding Hoàng Long RCP 8.5 0.001 0.02 — 0.001 0.04 CẢNG TNĐ Ho Chi River XI MĂNG SÀI Long Binh Quan 9 660.9 833.0 RCP 4.5 0.001 0.2 Minh Flooding GÒN RCP 8.5 0.001 0.2 Cảng xuất sét xi măng Song Typhoon Quang Yen Quang Ninh 682.0 719.4 — — — Vicem Hải Khoai Flooding Phòng

Cảng Hoàng Typhoon Kim Luong Kim Thanh Hai Duong 614.2 617.5 — — — Gia Flooding

Cảng Hải Thuy Typhoon Luu Ky Hai Phong 416.0 552.3 — — — Nam Nguyen Flooding

Dong Typhoon Minh Đức Hai An Hai Phong 388.8 513.4 — — — Hai 1 Flooding

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Table C.3 continued

AADF (tons per day) Hazard Climate Probability Name Commune District Province Min Max type scenario Min Max

Cảng thủy — 0.001 0.01 River nội địa Châu Chau Son Phu Ly Ha Nam 303.9 353.6 RCP 4.5 0.001 0.02 Flooding ơ S n RCP 8.5 0.001 0.02 — 0.001 0.02 NGỪNG RCP 4.5 0.001 0.1 HOẠT ĐỘNG (CẢNG NHÀ Phuoc River Nhon Trach Dong Nai 62.3 141.3 MÁY KHÍ Khanh Flooding ĐIỆN NHƠN RCP 8.5 0.001 0.1 TRẠCH 2)

Cảng Bến Typhoon An Hong An Duong Hai Phong 10.9 38.6 — — — Kiền Flooding — 0.001 0.01 River Cảng Kho RCP 4.5 0.001 0.02 Flooding trung chuyển Hoa Binh Vu Thu Thai Binh 10.2 30.8 RCP 8.5 0.001 0.02 xăng dầu Thái Bình Typhoon — — — Flooding

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Appendix D. Communes and Districts by Exposure of Road Networks to Natural Hazards

Table D.1. Twenty Communes Most Exposed to Flooding in Lao Cai Province

Flashflood susceptibility River flooding Landslide susceptibility Total Commune District km Current RCP 4.5 RCP 8.5 Current RCP 4.5 RCP 8.5 Current RCP 4.5 RCP 8.5 (2016) (2025) (2025) (2016) (2030) (2030) (2016) (2025) (2025)

Tong Sanh Bat Xat 9.2 46.0 46.0 20.4 41.6 41.6 41.6 7.8 7.8 19.7

Trung Leng Bat Xat 3.7 30.7 30.7 0.0 0.0 0.0 0.0 1.8 1.8 1.8 Ho

Nam Sai Sa Pa 6.6 21.2 21.2 21.2 0.0 0.0 0.0 9.3 9.3 16.3

Muong Vi Bat Xat 13.8 19.0 19.0 19.0 19.5 19.5 19.5 1.6 1.6 9.2

Nam Khanh Bac Ha 15.5 17.6 17.6 17.6 0.0 0.0 0.0 0.6 0.6 0.6

Coc San Bat Xat 16.2 17.0 17.0 2.4 10.2 10.2 10.2 7.2 7.2 16.2

Sa Pa Sa Pa 32.3 15.2 15.2 15.2 0.0 0.0 0.0 11.2 11.2 11.2

Ban Cai Bac Ha 23.5 14.2 14.2 14.2 0.0 0.0 0.0 3.2 3.2 3.2

Van Nam Xe 21.8 14.0 14.0 14.0 0.0 0.0 0.0 14.9 14.9 14.9 Ban

Den Sang Bat Xat 20.8 12.4 12.4 12.4 0.0 0.0 0.0 4.2 4.2 3.3

Ban Lien Bac Ha 49.3 10.9 10.9 10.9 0.0 0.0 0.0 1.8 1.8 1.8

Van Tham Duong 18.9 10.4 10.4 10.4 0.0 0.0 0.0 4.7 4.6 6.1 Ban

Y Ty Bat Xat 23.4 9.6 9.6 11.2 0.0 0.0 0.0 3.4 3.4 3.4

Van Nam Rang 15.2 9.2 9.2 9.2 0.0 0.0 0.0 10.0 10.0 10.0 Ban Van Duong Quy 30.8 9.1 9.1 9.1 0.0 0.0 0.0 2.1 2.1 2.1 Ban

Ta Phoi Lao Cai 35.7 8.4 8.4 7.0 16.5 16.5 16.5 11.1 8.6 8.7

Nam Cang Sa Pa 6.2 8.3 8.3 8.3 0.0 0.0 0.0 1.8 1.8 4.2

Lao Chai Sa Pa 11.6 8.3 8.3 8.3 0.0 0.0 0.0 20.8 20.8 20.8

Van Lang Giang 18.9 7.7 8.0 8.0 0.0 0.0 0.0 5.0 5.3 5.3 Ban

Ta Phin Sa Pa 20.2 7.2 7.2 7.2 0.0 0.0 0.0 14.3 14.3 14.3

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Table D.2. Twenty Communes Most Exposed to Flooding in Binh Dinh Province

Landslide Typhoon River flooding Total susceptibility flooding Commune District km Current RCP 4.5 RCP 8.5 Current Current (2016) (2030) (2030) (2016) (2016)

Thi Nai Quy Nhon 11.8 87.2 87.2 87.2 0.0 43.2

Nhon Hoa An Nhon 89.4 76.4 80.4 80.4 8.0 15.2

Ly Thuong Kiet Quy Nhon 15.7 78.9 78.9 78.9 0.0 0.0

Le Hong Phong Quy Nhon 16.1 71.6 71.6 71.6 0.0 0.0

Tay Xuan Tay Son 34.7 67.2 67.2 67.2 9.7 0.0

Phuoc Nghia Tuy Phuoc 33.5 55.0 55.0 55.0 9.8 14.2

Tran Phu Quy Nhon 6.5 60.4 60.4 60.4 0.0 0.0

Phuoc Hiep Tuy Phuoc 92.3 45.1 45.1 45.1 5.6 14.3

Nhon Binh Quy Nhon 68.6 41.2 41.2 41.2 0.0 20.9

An Tuong Dong Hoai An 38.5 33.9 33.9 33.9 37.3 0.0

Phuoc Thanh Tuy Phuoc 100.8 41.0 41.4 41.4 8.4 0.2

Tay Phu Tay Son 32.4 37.0 37.0 37.0 13.9 0.0

Canh Lien Van Canh 6.3 24.2 24.2 24.2 45.8 0.0

Nhon Tan An Nhon 38.6 35.2 35.2 35.2 8.0 0.0

Nhon Tho An Nhon 41.8 32.0 32.0 32.0 10.3 2.8

Phuoc An Tuy Phuoc 108.5 27.9 29.3 29.3 16.1 0.1

Vinh An Tay Son 5.0 25.8 25.8 25.8 18.5 0.0

Tran Hung Dao Quy Nhon 6.4 27.6 27.6 27.6 0.0 3.3

Binh Nghi Tay Son 89.6 24.3 24.3 24.3 12.7 0.0

An Dung An Lao 8.6 0.0 0.0 0.0 81.8 0.0

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Table D.3. Twenty Communes Most Exposed to Flooding in Thanh Hoa Province

Landslide Typhoon River flooding Total susceptibility flooding Commune District km Current RCP 4.5 RCP 8.5 Current Current (2016) (2030) (2030) (2016) (2016)

Dong Son Bim Son 47.7 96.9 96.9 96.9 1.7 0.0

Dong Xuan Dong Son 8.3 96.1 96.1 96.1 0.0 0.0

Thinh Loc Hau Loc 12.5 94.3 94.3 94.3 0.0 0.0

Hoang Long Thanh Hoa 10.4 91.8 91.8 91.8 0.0 0.0

Nga Thach Nga Son 37.5 87.0 87.0 87.0 0.0 3.1

Hoa Loc Hau Loc 34.7 87.7 87.7 87.7 0.0 0.0

Dong Tien Trieu Son 39.3 84.7 84.7 84.7 0.0 0.0

Rung Thong Dong Son 5.7 82.5 82.5 82.5 0.0 0.0

Dinh Tang Yen Dinh 51.5 81.3 81.3 81.3 0.3 0.0

Lam Son Bim Son 22.3 79.7 79.7 79.7 2.2 0.0

Dong Loi Trieu Son 37.4 78.7 78.7 78.7 2.8 0.0

Hau Loc Hau Loc 16.2 79.6 79.6 79.6 0.0 0.0

Xuan Lam Tho Xuan 22.7 76.9 76.9 76.9 0.5 0.0

Lien Loc Hau Loc 28.4 76.5 76.5 76.5 0.0 0.0

Ha Vinh Ha Trung 57.4 73.1 73.1 73.1 1.3 0.0

Hung Loc Hau Loc 36.6 71.5 71.5 71.5 0.0 3.3

Loc Tan Hau Loc 35.5 72.1 72.1 72.1 0.0 0.0

Nga Nhan Nga Son 19.3 72.0 72.0 72.0 0.0 0.0

Xuan Vinh Tho Xuan 42.2 72.0 72.0 72.0 0.0 0.0

Yen Ninh Yen Dinh 17.1 70.8 70.8 70.8 0.0 0.0

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